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Quantitative nuclear feature analysis in the prognosis of benign breast disease and ductal carcinoma… Susnik, Barbara 1994

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QUANTITATIVE NUCLEAR FEATURE ANALYSIS IN THE PROGNOSIS OFBENIGN BREAST DISEASE AND DUCTAL CARCINOMA IN SITUbyBARBARA SUSNIKMD, University of Ljubljana, 1988A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinTHE FACULTY OF GRADUATE STUDIESDepartment of PathologyWe accept this thesis as conformingto the required standardTHE UNIVERSITY OF BRITISH COLUMBIAJanuary1994© Barbara Susnik, 1993In presenting this thesis in partial fulfilment of the requirements for an advanceddegree at the University of British Columbia, I agree that the Library shall make itfreely available for reference and study. I further agree that permission for extensivecopying of this thesis for scholarly purposes may be granted by the head of mydepartment or by his or her representatives. It is understood that copying orpublication of this thesis for financial gain shall not be allowed without my writtenpermission.(Signature)Department of______________The University of British ColumbiaVancouver, Canada9Date jDE-6 (2188)ABSTRACTExcept of the diagnostic categories based on the morphology of cells andtissue there are currently no significant prognostic markers for patients withbenign breast disease or ductal carcinoma in situ (DCIS). This thesis proposedthat such prognostic information could be obtained based on quantitativenuclear feature analysis of diagnostic cells and/or normal appearing cells inbreast tissue. Image cytometry (IC) measurements provide numerousquantitative nuclear features which reflect the DNA content, nuclear morphologyand chromatin condensation patterns in stained nuclei. With the use of thistechnique it is possible to detect slight morphological nuclear changes which cannot be observed by the human eye. Measurements on tissue sections werechosen for this study because in these specimens visualization of tissuehistology and precise identification of affected glands is possible. The thesisinvolved testing of the following postulates:1) DNA ploidy can be evaluated by IC measurements of tissue sectionsDNA content measurements of archival breast specimens showed that theperformance of manual IC measurements of nuclei on tissue sections wascomparable to the results of flow cytometry and automated IC techniques.2) Quantitative nuclear features change in different histological patterns of breastdiseasesThe analysis of normal tissue, non-proliferative breast disease,proliferative breast disease, carcinoma in situ and invasive cancer specimensdemonstrated differences in nuclear features between different h istopatholog icalpatterns. Compared to normal tissue characteristics, the deviations of features11related to the nuclear area, shape, DNA content and chromatin texture allincreased with advancing morphological changes.3) Various histological types of DCIS can be characterized on the basis of thenuclear featuresThe differences in quantitative nuclear features were defined for varioushistological types of DCIS. The nuclear DNA content, size, irregularity of shapeand chromatin texture, all increased from the lowest values in cribriform type tothe highest values in comedo type DCIS. Aneuploidy was demonstrated inabout 60% of non-comedo DCIS and in about 95% of comedo DCIS.4) Differences in nuclear morphology, which may be related to the invasivepotential of ductal carcinomas in situ (DCIS), exist between I) pure DCIS, andii) DCIS with synchronous invasive carcinomaAn important task of this thesis was to obtain a prognostic indicator forductal carcinoma in situ (DCIS). Nuclear features of DCIS which wereassociated with the presence of invasive carcinoma in the surrounding breasttissue were identified. A classification system based on these nuclear featureswas then used to discriminate between cases with pure DCIS and cases of DCISwith invasive cancer in the surrounding tissue. This classification predictedaccurately the presence of invasive carcinoma in about 80% of non-comedoDCIS and in about 100% of comedo DCIS cases.5) Subtle changes of nuclear morphology exist in epithelial cells of normalappearing breast tissue adjacent to invasive carcinoma (malignancyassociated changes)The final goal was to test the hypothesis that small deviations in nuclearmorphology, indicative of the malignancy in the surrounding tissue, can bedetected through nuclear measurements of non-diagnostic, normal appearingiiicells in the vicinity of DCIS or invasive carcinoma. This phenomenon has beenpreviously studied in other tissues and has been described as a part ofmalignancy associated changes (MAC). The present study illustrated theexistence of MAC in the normal appearing breast tissue adjacent to in situ, orinvasive carcinoma. Patients with benign or malignant diseases could bedistinguished solely on the basis of the measurements of epithelial nuclei fromthe normal appearing glands. With the analysis of MAC about 85% of caseswere correctly classified as benign or malignant.In conclusion: A) Differences in nuclear features which weredemonstrated for various groups of breast diseases suggest that these featurescould be applied as an objective aid in the classification and diagnosis of breastdiseases. B) The analysis of DCIS nuclei can provide useful prognosticinformation making it possible to suspect the presence of invasive carcinoma inthe breast when only DCIS is present in the biopsy. Moreover, specific changesin nuclear morphology, which are characteristic of DCIS associated with invasivecarcinoma in the surrounding breast might be associated with the progressivecapacity of DCIS and may be helpful as a marker predictive of the subsequentbehavior of DIS tumors. C) Nuclear features, characteristic of MAC, areimportant as markers for occult malignancy in cases where only benign breastdisease is found in a biopsy. In addition, a high frequency of “MAC” nuclei in abenign breast tissue may be suggestive of a higher progressive potential and ofan increased risk for the development of invasive carcinoma from benign breasttissue which has a high frequency of “MAC” nuclei. In view of the clinicalrelevance of these findings it is very important to confirm and expand the resultswith further studies on larger number of patients.ivTABLE OF CONTENTSPAGEABSTRACT iiTABLE OF CONTENTS vLIST OF TABLES ixLIST OF FIGURES xiiLIST OF ABBREVIATIONS xviACKNOWLEDGMENTS xviii1. INTRODUCTION I1.1 Background I1.1 .A DNA content of breast carcinoma I1.1. B DNA measurements of invasive breast carcinomaperformed on archival tissues by flow and image cytometry 51.1 .C Analysis of DNA histograms obtained by image cytometry 61.1 .D Various nuclear features employed by imagecytometry 111.1.E Benign breast disease and premalignant changes 131.1. F Image cytometry of benign breast disease 161.1 .G Malignancy associated changes (MAC) 181.1.H Ductalcarcinomainsitu 191.1.1 Image cytometry of ductal carcinoma in situ 251.2 Proposal 27I .2.A Measurements of DNA content: Comparison ofdifferent cytometry techniques 27I .2.B The relationship between the histological patterns of breastdiseases and quantitative nuclear features 28VI .2.C Nuclear features as a prognostic factor for DCIS 29I .2.D Malignancy associated changes 312. OBJECTIVES 333. MATERIALS AND METHODS 343.1 Tissues 343.1 .A Comparison of image and flow cytometry 343.1 . B Comparison of image cytometry measurementsperformed on smears, cytospins and sections 343.1 .C Correlation of quantitative features with advancingmorphological changes 353.1 . D Heterogeneity of ductal carcinoma in situ 443.1 . E Differences between pure carcinoma in situ andcarcinoma in situ associated with invasive carcinomain the surrounding tissue 443.1.F Malignancy associated changes 443.2 Staining 463.2.A Flow cytometry 463.2.B Image cytometry 483.3 Image cytometry measurements 493.3.A Image cytometry of tissue sections 493.3.B Comparison of measurements performed ontissue sections, cytospins and smears 523.4 Description of main nuclear features 543.5 Analysis of DNA histograms 583.5.A Image cytometry 583.5.B Flow cytometry 59vi3.6 Statistics 614. RESULTS 654.1 The distribution of values in the image cytometry DNAhistograms of normal cells 654.2 Comparison of flow and image cytometry of tissue sections 674.3 Comparison of image cytometry measurements performedon smears, cytospins and sections 714.4 Correlation of quantitative features with advancingmorphological changes 794.5 Heterogeneity of ductal carcinoma in situ 864.5.A Comparison of comedo and non-comedo types 954.5.8 Quantitative nuclear features in different histologicaltypes of non-comedo DCIS 1004.6 Differences between pure carcinoma in situ and carcinomain situ associated with invasive carcinoma in the surroundingtissue 1114.6.A Non-comedo type 1144.6.B Comedo type 1234.7 Malignancy associated changes 1275. DISCUSSION 1405.1 Comparison of different cytometry techniques 1405.1 .A Flow cytometry and image cytometry of tissue sections 1425.1 . B Comparison of different image cytometry techniques I 445.2 The correlation between quantitative nuclear features andadvancing morphological changes 1465.3 Heterogeneity of ductal carcinoma in situ 149vii5.4 Differences between pure carcinoma in situ andcarcinoma in situ associated with invasive carcinoma insurrounding tissue 1515.5 Malignancy associated changes 1556. SUMMARY 1587. REFERENCES 161viiiLIST OF TABLESPAGETable 1. Different approaches for the ploidy analysis. 7Table 2. The number of cases analyzed in six groups of breastdiseases. 36Table 3. The number of pure DCIS and DCIS with adjacent invasivecarcinoma included in the study. 45Table 4. Cases involved in the analysis of malignancy associatedchanges. 47Table 5. Histogram parameters obtained from DNA histograms ofnormal cells. 66Table 6. Ploidy determined by image and flow cytometry. 70Table 7. Dl analysis of cases where FC and IC disagreed in ploidy. 72Table 8. Heterogeneity of tumors. 73Table 9. DNA histogram parameters of corresponding smears,cytospins and tissue sections. 80Table 10. DNA histogram parameters in six diagnostic groups. 83Table 11. The mean values of the representative features and theirvariances in six groups of breast diseases. 85Table 12. Ploidy in comedo and non-comedo DCIS. 96Table 13. DNA histogram parameters of different histological types ofDCIS. 97Table 14. Significant differences in nuclear features between noncomedo and comedo DC IS. 99Table 15. Jackknifed classification of non-comedo and comedo DCISnuclei. 102ixTable 16. The mean values of the representative features, and theirvariances, in different histological types of DCIS. 103Table 17. Ploidy of different histological types of non-comedo DCIS. 110Table 18. Average values of DNA histogram parameters obtained fromdifferent histological types of DCIS (with or withoutassociated invasive carcinoma in the surrounding tissue). 112Table 19. Ploidy of pure DCIS and DCIS associated with invasivecarcinoma in surrounding breast tissue. 113Table 20. Significant differences in nuclear features between noncomedo DCIS without invasion and non-comedo DCIS withadjacent invasive carcinoma. 116Table 21. Pure DCIS vs. DCIS with adjacent invasive carcinoma: noncomedo type. 118Table 22. Jackknifed classification of non-comedo DCISI and noncomedo DCIS2 nuclei. 121Table 23. Discrimination of non-comedo DCISI and non-comedoDCIS2 cases based on the proportion of DCIS2 nuclei onthe slides. 122Table 24. Significant differences in nuclear features between comedoDCIS without invasion and comedo DCIS with adjacentinvasive carcinoma. 126Table 25. Jackknifed classification of comedo DCISI and comedoDCIS2 nuclei. 130Table 26. Discrimination of comedo DCISI and comedo DCIS2 casesbased on the proportion of DCIS2 nuclei on the slides. 131xTable 27. Jackknifed classification of nuclei to normal nuclei and“MAC” nuclei (A). 135Table 28. Classification of benign cases and invasive carcinomacases according to the proportion of “MAC” nuclei. 136Table 29. Jackknifed classification of nuclei to normal nuclei and“MAC” nuclei (B). 137Table 30. Classification of benign and malignant cases (invasivecarcinoma and DCIS) according to the proportion of “MAC”nuclei. 139xiLIST OF FIGURESPAGEFigure 1. A variety of features can be measured by image cytometry. 3Figure 2. Phases of the cell cycle and a DNA histogram of normalbreast tissue. 9Figure 3. Examples of diploid , tetraploid and aneuploid peaksin DNA histograms. 10Figure 4. Breast diseases with an increased risk of subsequentinvasive carcinoma. 15Figure 5. Ductal carcinoma in situ without invasive component(DCIS-) and ductal carcinoma in situ with invasivecomponent (DCIS+). 30Figure 6. MAC:malignancy associated changes. 32Figure 7. Normal breast lobule. 37Figure 8. Non-proliferative breast disease: mild hyperplasia. 39Figure 9. Proliferative breast disease (A). 40Figure 10. Proliferative breast disease (B). 41Figure 11. Ductal carcinoma in situ: comedo type. 42Figure 12. Invasive ductal carcinoma. 43Figure 13. Image cytometry device, which was used for themeasurements on tissue sections. 50Figure 14. An example of normalized histogram and histogramparameters. 60Figure 15. Statistical analysis based on cell by cell discrimination wasused for the classification of patients. 64xiiFigure 16. Examples of aneuploid and diploid IC and FC histogramsof five carcinoma cases. 68Figure 17. Comparison of flow and image cytometry on the basis ofthe DNA index. 69Figure 18. DNA index of invasive carcinoma and ductal carcinoma insitu. 74Figure 19. Nuclear area vs. IOD scatter plots of a smear and acorresponding cytospin and tissue section. 75Figure 20. Micrograph of nuclei from a smear and a tissue section. 76Figure 21. DNA histograms of corresponding smears, cytospins andtissue sections (A). 77Figure 22. DNA histograms of corresponding smears, cytospins andtissue sections (B). 78Figure 23. Nuclear area vs. lOD scatter plot of various histologicalpatterns present on the same slide. 82Figure 24. Nuclear area vs. lOD scatter plots of six groups of breastdiseases. 87Figure 25. Variation of radius vs. variance of lOD scatter plots of sixgroups of breast diseases. 88Figure 26. Cribriform DCIS. 89Figure 27. Confluent DC IS. 90Figure 28. Papillary DCIS. 91Figure 29. Comedo DC IS. 92Figure 30. Area vs. IOD scatter plots of various DCIS present on thesame slide: case A. 93xiiiFigure 31. Area vs. IOD scatter plots of various DCIS present on thesame slide: case B. 94Figure 32. Micrograph of comedo and non-comedo nuclei. 98Figure 33. Comedo vs. non-comedo nuclei: the relationship betweenthe number of employed features and the rate of correctclassification. 101Figure 34. Nuclear area in different DCIS types. 105Figure 35. lCD in different DCIS types. 106Figure 36. Variation of radius in different DCIS types. 107Figure 37. Variation of optical density in different DCIS types. 108Figure 38. Variance of lCD in different DCIS types. 109Figure 39. Examples of DCISI and DCIS2 nuclei: non-comedo type. 115Figure 40. Non-comedo DCISI vs. non-comedo DCIS2 nuclei: therelationship between the number of employed features andthe rate of correct classification. 120Figure 41. Classification of DCISI and DCIS2 cases: Noncomedotype. 124Figure 42. Examples of DCISI and DCIS2 nuclei: comedo type. 125Figure 43. Comedo DCISI vs. comedo DCIS2 nuclei: the relationshipbetween the number of employed features and the rate ofcorrect classification. 128Figure 44. Classification of DCISI and DCIS2 cases: Comedo type. 129Figure 45. Examples of normal nuclei from benign cases and normalnuclei from invasive carcinoma cases. 132xivFigure 46. Normal vs. “MAC” nuclei: the relationship between thenumber of the employed features and the rate of correctclassification. 134Figure 47. Classification of malignant and benign cases. 138xvLIST OF ABBREVIATIONSADH: disease: atypical ductal hyperplasiaADH: nuclear feature: average distance of the high density chromatinfrom the nuclear centerADL: average distance of the low density chromatin from the nuclearcenterADM: average distance of the medium density chromatin from thenuclear centerBDYI: coarse boundary variationBDY2: fine boundary variationCIS: carcinoma in situC-MASS center of mass of the nucleusC-MASSL center of mass of the light density chromatinCRH: high density chromatin compactness ratioCRL: low density chromatin compactness ratioDC IS: ductal carcinoma in situDCISI: ductal carcinoma in situ without invasive carcinoma in thesurrounding breast tissueDC 1S2: ductal carcinoma in situ with synchronous invasive carcinoma inthe surrounding breast tissueDI: DNA indexDNA: deoxyribonucleic acidFAREAI: fractal area IFAREA2: fractal area 2FC: flow cytometryxviHAER: high average extinction ratioIC: image cytometryIOD: integrated optical densityMAC: malignancy associated changesMAER: medium density chromatin average extinction ratioMHAER: medium/high density chromatin average extinction ratioNH: number of high density chromatin clustersNL: number of low density chromatin clustersNM: number of medium density chromatin clustersOD MAX: optical density maximum00 SKEW: skewness of the optical density distributionOD VAR: variation of optical densityTARH: total area ratio for the high density chromatinTARL: total area ratio for the low density chromatinTARM: total area ratio for the medium density chromatinTERH: total extinction ratio for the high density chromatinTERL: total extinction ratio for the low density chromatinTERM: total extinction ratio for the medium density chromatinVAR RAD: variation of radiusV AREA: variance of areaV 100: variance of integrated optical densityV NM: variance of the number of medium density chromatin clustersxviiACKNOWLEDGMENTSI am very grateful to my supervisors Branko Palcic and Jean LeRiche forall their support and for making my work possible. I would like to express mygratitude to Ann Worth who reviewed all the slides and helped me to betterunderstand breast pathology. I would also like to thank to many others whohelped me during the past four years: Alan Harrison, Yvonne Zheng, CalumMacAulay, Jasenka Matisic, Jagoda Korbelik, Paul Lam, David Garner, and NealPoulin.xviii11. INTRODUCTION1.1 BACKGROUND1.1 .A DNA content of breast carcinomaBreast cancer is one of the most frequently diagnosed cancers inEuropean and North American women. At present more women die from thisdisease than from any other malignancy with the exception of lung cancer.Breast cancer accounts for about 30% of newly diagnosed cancer cases and for20% of cancer deaths in women (Annual report, BC CA, 1990/1991). Nearlyevery ninth woman gets breast cancer once in her lifetime and almost half of theaffected persons die from this disease.At present, various factors are used to divide the heterogeneous group ofbreast carcinomas into distinctive prognostic subgroups (McGuire 1990, Elledge1992). Established prognostic factors for breast carcinoma are tumor stage(defined by its size, lymph node involvement, and the presence or absence ofmetastasis), histological type, tumor grade, and the presence of estrogen andprogesterone receptors. It is believed that other factors, such as tumor ploidyand proliferation markers, are important in breast cancer prognosis, but their roleis not entirely clear. In addition, there is a variety of new potential prognosticmarkers, such as proteases, growth factors or the expression of variousoncogenes, and these are at present the subject of numerous studies.Ploidy of the tissue is related to quantitative chromosome changes whichcan be demonstrated by cytogenetic analysis. The DNA ploidy of breastcarcinoma reflects the DNA amount in the nuclei. Two techniques that are2commonly used for the quantitative assessment of the nuclear amount of DNAare flow cytometry (FC) and image cytometry (IC).The basic principle of FC is that the suspension of nuclei is labeled with aspecific fluorescent DNA dye which stains the DNA in a stoichiometric way. Thecell suspension then flows in a stream, so that single nuclei pass a laser beamat the observation point. The laser induces fluorescence of the dye, which canbe measured. The fluorescence signal is proportional to the total DNA amountin the nucleus. The size of nuclei can also be measured and is determined bythe light scattering from the nuclear surface.IC DNA measurements are based on measurements of optical density (oremitted fluorescence) of nuclei in cytological or histological specimens which aredeposited on microscope slides and stained with a stoichiometric stain. Inaddition to the DNA content measurements, IC makes it possible to measuremany other nuclear features related to nuclear size, shape, roughness of thenuclear boundary, and most importantly, a variety of texture features describingthe DNA distribution in the nucleus (Figure 1).The advantage of FC is in the fast analysis of large populations of cells.This provides good resolution of the DNA content histograms with lowcoefficients of variation and assures statistical reliability. The disadvantage ofFC is that the morphologic identification of different cell classes is not possiblewithout additional labeling. The cells of interest are diluted with non-tumor cellpopulations (inflammatory cells, stromal cells, or normal cells from thesurroundings of the tumor) and the extent of this dilution can not always berecognized.MEASUREMENTSOFNUCLEARFEATURESMAYBEPREDICTIVEOFTHEPROGNOSISOFBREASTDISEASES.SIZESHAPEDNAAMOUNTDNADISTRIBUTION0Figure1.AVARIETYOFNUCLEARFEAUJRESCANBEANALYZEDBYIMAGECYIOMETRY.4Image analysis on the other hand, allows visualization and morphologicidentification of the cells. A trained operator can selectively collect cells ofinterest, or can interact later in the procedure by classifying the cells on theautomatically collected images. With the use of IC it is therefore possible todiscover small cell populations with abnormal DNA content, which would remainunnoticed when analyzed by FC. The disadvantage of IC is in the labor intensivenature of the analysis and in the relatively small number of cells that can beanalyzed in a given time frame. This results in histograms with generally muchlower resolution when compared to FC histograms.The two techniques have been previously compared with an attempt torelate the DNA index or the DNA ploidy, acquired with IC or FC analysis of freshtissue. A correlation has been demonstrated in about 90% of cases (Bauer1990, Elsheikh 1992). The results of both techniques have also been found tocorrelate well in studies using isolated nuclei from fresh or frozen breast tissueor from breast aspirates (Auer 1985, Cornelisse 1984, Cornelisse 1985, Stal1986, Fallenius 1987, Wilbur 1990, Falkmer 1990, Baldetorp 1992, Ghali 1992).Various cytometry studies have been carried out on breast cancer tissuewith an attempt to understand the significance of DNA content in ‘prognosis. Inthese studies FC (Ewers 1984, Cornelisse 1987, Kallionemi 1987, DressIer1988, Clark 1989, Beerman 1991, Fisher 1991) or IC was employed (Auer 1984,Fallenius 1988, Bocking 1989, Uterlinde 1988, von Rosen 1989, Troncosco1989, Haroske 1991, Theissig 1991). Most, but not all, authors reported acorrelation between aneuploidy and bad prognosis. However, the significance ofDNA content in the prognosis of breast cancer is not yet fully understood. Itseems that additional investigations will be necessary before the DNA content of5breast cancer will be commonly accepted as a prognostic factor in clinicalpractice.1.1.B DNA measurements of invasive breast carcinoma performedon archival tissues by flow and image cytometry techniquesThe analysis of archival tissues is very convenient for retrospectivestudies of prognostic markers where a long follow-up and a large number ofpatients are essential. Retrospective analysis is the method of choice inevaluating the DNA content in prognosis of various cancers and breast cancer inparticular.IC of embedded tissue can be performed on isolated nuclei or on tissuesections. Studies comparing FC and IC of nuclei isolated from embeddedtissues use a similar disaggregation procedure for both techniques (Hedley1983). Nevertheless, aneuploid populations, undetected by FC, could often bedetected by IC where representative cells were selectively chosen for themeasurements (Rodenburg 1987, Bose 1989). Rare comparative studiesdemonstrate disagreements between the results of FC and IC performed ondisaggregated nuclei from embedded breast tissue (Carpenter 1988, Ellis 1989,Roos 1989).Larger discrepancies can be expected when FC is compared to ICperformed on tissue sections because the tissue preparation methods arecompletely different. A limited number of studies have used IC of tissue sectionsin comparison to FC of embedded tissue from breast (Uyterlinde 1989) or fromother tissues (Kreicbergs 1981, Bauer 1986, Cope 1991). The authors found6relatively large discrepancies and reported difficulties with the interpretation oftissue section histograms. Moreover, it has been suggested that histologicalsections of embedded breast cancer tissue are associated with largemethodological problems and are not suitable for DNA measurements (Berryman1984).1.1.C Analysis of integrated optical density histograms obtainedby image cytometryAmong nuclear features measured by IC, the most widely used feature isintegrated optical density (IOD). Optical density (OD) is proportional to theamount of light which is absorbed by a stained object and is proportional to thedensity of the stain. With the use of the DNA stoichiometric stain, the OD of apixel is a function of the amount of the DNA in the pixel. lCD is the sum of theCD values of all pixels in a nucleus and is therefore proportional to the nuclearDNA content.Aneuploid nuclear DNA content, as measured by cytometric techniques,is thought to be associated with poor prognosis in various tumors (Auer 1989,Atkin 1991). DNA ploidy measured by cytometry techniques is related to theploidy determined by the cytogenetic analysis (Table 1). With FCmeasurements, as well as with IC measurements of smears or disaggregatedtissue, the whole nuclei are analyzed. The DNA content of the nuclei can beobtained with these techniques. IC of tissue sections measures the DNA contentof sectioned nuclei. The total DNA content of whole nuclei is reflected in the7Table 1. DIFFERENT APPROACHES FOR THE PLOIDY ANALYSISTECHNIQUE WHAT IS ANALYZED?CYTOGENETICS NUMBER AND TYPE OF CHROMOSOMES IN NUCLEIFC DNA AMOUNT IN NUCLEIIC DNA AMOUNT IN NUCLEIIC OF TISSUE SECTIONS DNA AMOUNT IN SECTIONS OF NUCLEIThe nuclear DNA content (DNA ploidy) can be obtained with cytometrymeasurements of whole nuclei in smears or cytospins. With themeasurements of tissue sections the amount of DNA of cut nuclei isanalyzed.8DNA content of sectioned nuclei. This depends on the section thickness and thenuclear size.Generally, nuclei in normal tissues contain a double set of chromosomes(2c, n=1) and are said to be in the diploid range. In normal tissues there isusually a certain proportion of cells which proliferate in order to replace thedying cells. The proliferating cells follow the events of the cell-cycle which isdivided into four phases: I) G1 -phase, where cell performs its usual function, ii)S-phase, with the DNA synthesis, iii)G2-phase, where the additional preparationfor the cell division takes place, and iv) M (mitotic) phase with the division ofnuclear material and cytoplasm to two daughter cells (Figure 2A). Non-cyclingcells rest in the G0- phase and have a diploid (2c, n=1) DNA amount and so dothe cells in theG1-phase of the cycle. In the S-phase the cells double their DNAamount and during the G2-phase and mitosis the cells contain a duplicatedamount of DNA with four sets of chromosomes (4c, n=2).The events of the cell cycle are manifested in the lOD histograms (Figure2B). The DNA distribution can be analyzed in the IOD histograms bydetermining boundaries for diploid, tetraploid and aneuploid peaks or in a moreobjective fashion by defining histogram statistics (Fallenius 1988, Opfermann1987, Bocking 1989, Stenkvist 1990).One way to define the DNA ploidy from the IOD histogram is with the useof the DNA index which corresponds to the modal value of the histogram peak(Figure 3). In a DNA diploid pattern histogram, which resembles normal tissue,the Go/Gi peak is found in the peridiploid area (2c, n=1) with some cells in the 5-phase and some cells in the G2M-phase (4c, n=2). In tetraploid histograms themajor cell population is in the tetraploid region (4c). Histograms with peaks9A)S phaseG2 phasemitosisGO/Gi phaseB) CONTROL CELLS (lymphocytes)R’\\\N CELLS OF INTEREST (normal tissue)24.1812600C‘I0IEC)IPLOID PEAJC-cdl. In 01/00 pho..(normal DNA amount)CELLS IN 02—PHASE AND WITOSIS(doubl.d DNA amount)S—phaseNNNNNNNNNNNNNI 2nuclear DNA content:3 4 5POD—cells of interestPOD—lymphocytesFigure 2a and 2b. PHASES OF THE CELL CYCLE (a) and DNA HISTOGRAM OF NORMAL BREAST TISSUE (b).10403020100DIPLOID HISTOGRANCONTROL CELLSTUMOR CELLS2 3DNA CONTENTSU20IsJa2a6045w-IUz 300w05Dz04030Ui-JUa20Ui0Sa100jTETRAPLOID HISTOGRAM2 5DNA CONTENTANEUPLOID HISTOGRAMLaJ2 3DNA CONTENT4 5Figure 3. EXAMPLES OF DIPLOID , TETRAPLOID AND ANEUPLOID PEAKS IN DNA HISTOGRAMS.11outside the diploid or tetraploid area are considered aneuploid. Based on theDNA ploidy profile the histograms can be classified into four types l-IV (Auer1980).There are other approaches to the evaluation of IC-DNA histograms ofbreast cancer in an objective way. Quantitative histogram descriptors, whichmight have a prognostic value for breast carcinoma, are: Mean lOD (equals theaverage nuclear DNA content of the measured cell population), 2c-deviationindex, DNA-malignancy grade, percentage of cells exceeding various levels ofDNA content, entropy of the histogram, and ploidy balance (Opfermann 1987,Fallenius 1988, Bocking 1989, Stenkvist 1990).1.1. D Various nuclear features employed by image cytometryThe morphological characteristics of nuclei as seen by the pathologist incytological or histological specimens are among the most important criteria inthe evaluation of tumors. Genetic alterations have an important role in thedevelopment and progression of malignant tumors (Feinberg 1982, Nowell1989). Structural DNA changes are associated with changes in the chromatinpattern and alterations in nuclear morphology (Pienta 1989).One of the advantages of IC is, that in addition to DNA content, severalother nuclear features expressing morphologic nuclear characteristics in aquantitative way can be measured. Nuclear features can be categorized as: i)morphometric features, describing the nuclear size and shape, and ii) texturefeatures, which describe the distribution of DNA in the nucleus. Examples of12morphometric features are nuclear area, and various shape features, such ascompactness, variation of nuclear radius, and elongation.In the past years several new features have been developed with theemphasis on the textural features (Komitowski 1985, MacAulay 1989). Thealterations in the nuclear chromatin structure in fixed cells or tissues reflect thestate of chromatin organization at the time of fixation and can be measured andreported in the form of texture features. Texture features can be divided intocontinuous and discrete texture features. Examples of continuos texturefeatures are the variance of optical density and contrast. Discrete texturefeatures are related to the DNA condensation and the distribution ofheterochromatin and euchromatin in the nucleus. Discrete features are basedon the separation of chromatin into low, medium and high density chromatin withthe use of thresholds. Examples of this type of texture features are: Number ofhigh density chromatin pixels, total area of medium density chromatin pixels, andaverage distance of low density chromatin pixels from the object center.The use of various types of nuclear features for diagnostic or prognosticpurposes in tumor pathology may offer superior results, as compared to DNAcontent alone. The value of using multiple nuclear features (DNA ploidy, textureand morphometric features) in grading of breast cancer was recognized in manystudies (Stenkvist 1978, Larsimont 1989a, Baak 1985, Umbricht 1989, Dawson1991, Komitowski 1990, Theissig 1991). It has been shown that nuclear gradeof breast carcinoma correlated better with other prognostic factors when it wasaided by image analysis of nuclear morphology and chromatin pattern(Komitowski 1990). Quantitative grading seemed to be more reproducible andgave better predictive values (Theissig 1991). Estrogen receptor positive and13negative tumors could be distinguished on the basis of different nuclear featuressuch as area, DNA content and certain texture features (Larsimont I 989b). Inaddition to the prognostic implications, this finding is interesting, because itshows the ability of IC to detect morphologic nuclear changes which are relatedto the altered biology of the cell.1.1 .E Benign breast disease and premalignant changesThe term benign breast disease encompasses a wide spectrum of benignconditions. Current understanding of these diseases has been described indetail (Harris 1991, Page 1987). It is suspected that some benign breastdiseases are associated with an increased risk of subsequent breast carcinoma.The relationship between benign breast diseases and subsequent breast cancerhas been the subject of numerous studies. However, it has not yet beendemonstrated if they are the precursors of invasive cancer, or just the markers ofincreased risk of developing invasive carcinoma at another site.A continuum of morphologic changes between normal lobules and ductalcarcinoma in situ (DCIS) has been described by Wellings (Wellings 1975).These changes have been characterized by hyperplasia and varying degrees ofatypia. In the above study, breasts with invasive cancer and breastscontralateral to cancerous breasts were obtained by mastectomy. Tissues werethoroughly examined by a subgross sampling technique, and compared tobreasts without invasive carcinoma, which were obtained from autopsies. Theirresults showed that DCIS and atypical proliferative changes were much morefrequently present in cancerous and contralateral breasts than in breasts without14carcinoma. Also, the number of such lesions in cancer-affected breast wasmuch higher than in breasts without carcinoma. A similar study performed on alarger sample, reported additional foci of DCIS (unrelated to invasive cancer) in52% of breasts with carcinoma, 48% of contralateral breasts and in only 6% ofbreasts from random autopsies (Alpers 1985). Moreover, proliferative lesionswith severe atypia showed a similar tendency to appear in cancerous breasts.These findings support the association between i) proliferative changes withsevere atypia and invasive carcinoma, and ii) between DCIS and invasivecarcinoma.Retrospective studies of women with benign breast biopsies indicate thatsubgroups of women with benign breast disease have an increased relative riskof getting breast cancer as compared to a general population matched for age(Black 1971, Dupont 1985, Page 1985, Page 1986, Jensen 1989, Tavassoli1990, Palli 1991, London 1992, Dupont 1993). These epidemiological studiessupport the division of benign breast diseases according to their increasedcancer risk to three groups: i) non-proliferative breast disease with no increasein the risk, ii) proliferative breast disease with 2 times increased risk, and iii)atypical hyperplasias with 4-5 times increased relative risk of subsequentinvasive carcinoma (Figure 4).Non-proliferative breast disease includes mild hyperplasia, cysts,calcifications, fibroadenomas and papillary apocrine change. Proliferativebreast disease without atypia consists of moderate and severe epithelialhyperplasia, papillomas and sclerosing adenosis. Proliferative changes withatypia consist of atypical epithelial hyperplasias. Cellular and architecturalfeatures of epithelial hyperplasia classified by this approach have beennormal tissueandnon-proliferativebreastdiseaseproliferativebreastdiseaseproliferativebreastdiseasewithatypiaaltermiductlumenepitheliacellsbasalmembaneductalcarcinomainsitu•....—p..invasivecarcinomaINCREASEDRISKOFSUBSEQUENTINVASIVECARCINOMAFigure4.BREASTDISEASESWITHANINCREASEDRISKOFSUBSEQUENTDEVELOPMENTOFINVASIVECARCINOMA.16described in detail by Page (Page 1987). This approach supports the continuumbetween hyperplasia, atypical hyperplasia and CIS in terms of morphology andin terms of the risk of subsequent invasive cancer. Unfortunately, variations indiagnosis in this area of pathology are frequent (Temple 1989) and the inter-observer variability of the diagnoses is high even when tested amongpathologists highly experienced in breast pathology (Rosai 1991, Schnitt 1992).It is obvious that the assessment of risk associated with a particulardiagnosis is unreliable if the diagnosis itself is uncertain. Nuclear features maybe useful as an aid in the evaluation of premalignant breast changes to increasethe objectivity and the reproducibility of the diagnosis.1.1 .F Image cytometry of benign breast diseaseThe process of malignant transformation of breast tissue is thought to bea sequence of molecular events which are associated with the alterations intissue morphology of normal tissue to proliferative/hyperplastic changes, DCISand finally to invasive ductal cancer. It should be possible to follow thesequence of these changes by the analysis of quantitative nuclear features,which reflect the changes in the DNA amount, nuclear size and shape, andchromatin structure. Similar progressive changes in nuclear size, nuclearshape, chromatin texture and/or in the DNA content have been demonstrated byIC in other tissues, such as cervix, colon, thyroid, and prostate (Bibbo 1989,Mulder 1992, Petein 1991, Salmon 1992, Wang 1992).Analysis of nuclear features has not been performed on breastpremalignant changes and DCIS as widely as on invasive cancer. King et aI.17(1988) demonstrated the ability of IC to discriminate between normal,premalignant, CIS and invasive cancer cells on breast fine needle aspiratesaccording to the diagnoses from the subsequent surgical biopsies. Moreover,with an appropriate statistical model, they have been able to classifypremalignant lesions which could not be identified by routine cytology. With ICclassification, which was based not only on the DNA content but on variousnuclear features, the changes were clustered into a clinically insignificant group(mild and moderate hyperplasia) and into a clinically significant group (atypicalhyperplasia, CIS and invasive cancer). Their results support the theory that likeCIS, atypical hyperplasia is also a precursor of invasive carcinoma.In another study, where IC was employed on breast tissue sections, acomparison of DNA content of preneoplastic (atypical hyperplasia) andneoplastic lesions demonstrated that the DNA ploidy acquired from tissuesections with preneoplastic changes was identical to the DNA ploidy ofsynchronous carcinoma (Teplitz 1990). In another study, nuclear area, nuclearperimeter and DNA content were measured for usual hyperplasia, ADH andDCIS (Norris 1988). These features were not found useful in differentiatingbetween the histological groups. (It is possible that positive results could beobtained by the incorporation of chromatin distribution features in this study).Only DCIS with high grade nuclei could be separated from the other groups byusing these two features. To the contrary, Crissman et al. (1990) foundaneuploidy more often in DCIS (71%, n=25) than in ADH (36%, n=35). Howeversuch difference in a single feature can not be sufficient for a successfulclassification.18It was also shown that epithelial cells in the proliferative areas offibrocystic disease are sometimes aneuploid (lzuo 1971). Proliferative changesof patients with fibrocystic disease who later developed invasive cancer werefound to be aneuploid more often than proliferative changes of patients withoutsubsequent invasive cancer. The results of the above study support theassociation of aneuploidy and the progressive potential of benign breastdisease.Another study demonstrated that hyperplastic changes associated withductal carcinoma in situ exhibited higher mitotic counts than hyperplastic lesionsin benign proliferative disease (DePotter 1987). This finding indicates that ahigher proliferative activity might be predictive of the ductal malignancy in thesurrounding tissue.1.1 .G Malignancy associated changes (MAC)Malignancy associated changes (MAC) are defined as subtlemorphological nuclear changes in normal appearing tissues adjacent or distantto malignant tumors. The first reports on MAC began in the late sixties asqualitative observations. The characteristics of chromatin structural changes,which were first defined as MAC, were first described by Nieburgs (1968). Heclaimed that malignant tumors are associated with systemic cellular changeswhich can be recognized under high magnification of the light microscope by anexperienced observer. MAC have been reported on nuclei of peripheral bloodcells, buccal smears, sputum, bone marrow, uterus, pancreas, liver and skin19epithelia from patients with adjacent or distant malignancies (Nieburgs 1967,Nieburgs 1968, Finch 1971, van Oppen Toth 1975).With the development of image cytometry devices, MAC studies becamemore objective and reproducible. Normal appearing nuclei originating fromtissue adjacent to malignancy were analyzed and compared to normal nucleifrom the same type of tissue of patients without cancer. Most of these studieswere performed on material from cervix (Weid 1984, Montag 1989, Haroske1990, Bibbo 1989, Zahniser 1991, Hutchinson 1992), colon epithelium (Bibbo1990, Montag 1991), lung (Swank 1989), and thyroid (Bibbo 1986, LermaPuertas 1989). Majority of these studies described the changes of nuclearshape and chromatin distribution in normal appearing nuclei from the tissueadjacent to carcinoma. One of the authors used flow cytometry, and reportedthe presence of higher proliferation rate and even aneuploidy in nuclei of normalcolon tissue, as far as 10 cm away from carcinoma (Ngoi 1990).However, there have been no similar observations reported on breasttissue, except that higher proliferative activity has been demonstrated on nucleifrom benign hyperplastic changes in the neighborhood of carcinoma in situ(DePotter 1987).1.1.H Ductal carcinoma in situ (DCIS)Breast carcinoma in situ (C IS) consists of a heterogeneous group oflesions (Harris 1991, Schnitt 1988). Based on the cytological appearance andthe pattern of growth, CIS is classified to two major types: Lobular and ductalCIS. Ductal carcinoma in situ (DCIS) is characterized by proliferation of20malignant cells in the ducts without evidence of invasion across the basementmembrane. DCIS constitutes the majority of in situ breast cancer, about 80%.There is a variety of histological types of DCIS with different growthpatterns. Often different histologic types are found in the same biopsy. Themost common histological types are cribriform, solid, micropapillary, papillaryand comedo (Page 1987, Schnitt 1991). Various types of DCIS differ not only intheir morphology but also in their clinical appearance, biological characteristicsand malignant potential.DCIS can be detected clinically as palpable lumps, with mammography ascalcifications or a mass and incidentally when the breast is examined for otherdiseases. The detection of early stage cancers ensures better prognosis andfacilitates the breast preserving approach in therapy. The frequency of DCIS inwomen who had breast malignancy detected by mammography is four timeshigher than the frequency of DCIS in women who present with palpable breastcarcinoma (Stacey-Clear 1992). Since mammography screening has become aroutine the detection of carcinoma in situ without associated invasive carcinomahas increased from less than 5% to 15-22% of cancers found in breast biopsies(Anderson 1991, McKinna 1992). One study reported that clinically occult DCISwere found in autopsies of middle aged women in 15% of cases (Nielsen 1987).This study indicates that some DCIS cases are not detected in a woman’s lifetime and that the true incidence of DCIS is even higher than detected by themammography screening.The natural history of DCIS is not well understood because it is usuallyremoved after the diagnosis. This situation is similar to carcinoma in situchanges in other tissues, with the best example in cervical CIS (Koss 1963, Koss211989). It has become clear that not every DCIS is a life threatening disease andthat a proportion of these lesions persist unchanged for long periods of time oreven regress.At present it is known that the risk for the development of subsequentinvasive cancer from small DCIS, detected incidentally, is substantially elevatedwhen compared to the general population matched for age. Two retrospectivestudies evaluated the prognosis of small DCIS which were missed in the initialdiagnosis and were therefore treated only with biopsy. Both have shown anincreased risk of subsequent invasive carcinoma in these patients. The firststudy presented the follow-up results of 15 such lesions (Rosen 1980). In theaverage time of 9.7 years, 10 women (66%) developed cancer in the samebreast. Recurrent carcinomas were ductal in origin and invasive in 8 cases.This group of patients had 20% mortality from breast cancer (3 patients). Thesecond study followed 25 cases of DCIS treated only by biopsy for average of 16years. Seven women (28%) presented with invasive cancer in the same breastthree to ten years after the initial biopsy (Page 1982).In the prospective study of Lagios, small DCIS was treated only withexcision and a relatively small recurrence rate (3/20) was found. However, thefollow-up period in this study was only 44 months (Lagios 1982). On a largersample of 79 patients with DCIS detected by mammography and treated onlywith biopsy, 10% recurrence has been described with an average follow-up timeof 4 years (Lagios 1989). One half of the recurrent cases were DCIS and theother half were invasive carcinomas. The recurrence was associated with thehigh nuclear grade of DCIS and mostly occurred with comedo DCIS. Another22prospective study reported 23% (5/22) recurrence of DCIS treated only bylumpectomy with the average of 39 months follow-up after surgery (Fisher 1986).The above studies point to the heterogeneity of DCIS and indicate thatDCIS are common but not necessarily dangerous changes and do not need tobe treated aggressively in every case. However, there is a lack of prognosticfactors which would predict the destiny of individual patients. In the past,mastectomy used to be the treatment of the choice for DCIS. Mastectomy canbe justified by I) frequent occult invasion; ii) frequent DCIS multicentricity whichis not identified clinically or with mammography, iii) possible malignanttransformation of the remaining normal tissue (Ashikari 1977, Schnitt 1988,Lagios 1982). Mastectomy provides nearly 100% cure for DCIS, but at the sametime it represents overtreatment for many patients. It is clear that a betterunderstanding of the biology and natural history of DCIS is required in order toimprove the appropriateness of treatment.Studies that have used a breast conserving approach for the treatment ofDCIS included a relatively small number of patients and provided differentconclusions. Some authors found conservative surgery for DCIS with or withoutadditional radiotherapy acceptable with the rationale that recurrence could besuccessfully treated with more radical treatment, while others reported anunacceptable recurrence rate following a local excision (Fischer 1986, Kinne1989, Arnesson 1989, Carpenter 1989, Price 1990, Bornstein 1991). Localexcision appears to be an adequate treatment for incidentally detected DCIS andfor the non-comedo subset of mammographically detected DCIS. Comedo DCISwas very seldom discovered incidentally and was often associated with therecurrence after conservative treatment (Schwartz 1992).23Two characteristics of DCIS present a risk to breast conserving therapy:multicentricity and occult invasion. Many studies demonstrated that DCIS is amultifocal disease with the increasing size of the lesion being predictive ofmulticentricity as well as occult invasion. Multricentricity has been be detectedin 30 - 68% of breasts removed because of a biopsy diagnosis of DCIS (Ashikari1977, Rosen 1980, Lagios 1982, Silverstein 1987, Patchefsky 1989). In contrastto these findings, a study by Holland reported that DCIS typically did not have amulticentric pattern but showed a continuous growth (Holland I 990A).After the diagnosis of DCIS in a biopsy, the subsequent mastectomyspecimens show coexistent invasive carcinoma in 6% - 42% of cases dependingon the precision of the method (Rosen 1980, Carter 1977, Lagios 1982,Patchefsky 1989). DCIS detected incidentally in a biopsy performed for otherdisease are usually not associated with invasive foci in the remaining breast.The rate of microinvasion depends on the histological type of DCIS (Patchefsky1989). Microinvasion is often found with comedo type, even with the smallDCIS. In contrast, cribriform, solid and papillary types are rarely associatedwith microinvasion. The micropapillary type has an intermediate frequency ofmicroinvasion.At present the factors which determine the progressive potential of DCISare not understood. Therefore, it is not possible to predict the high risk ofrecurrence or progression to invasive cancer for individual patients. It is knownthat the histological type of DCIS has an effect on prognosis. Other factors,which are also thought to have the effect on prognosis of DCIS, are i) size, ii)grade, iii) proliferation rate, iv) aneuploldy, v) hormone receptors, vi) c-erbB-2overexpression, vii) altered expression of p53 gene, and others (Meyer 1986,24van de Vijver 1988, Locker 1990, Bartkova 1990, Killeen 1991, Bur 1992, Pallis1992, Schimmelpenning 1992, Poller 1993).Most of these potential prognostic markers for DCIS are related to ahistological type of DCIS. The aggressive nature of comedo DCIS is well known(Lagios 1989, Schwartz 1992). This type of DCIS is most often aneuploid(Locker 1990, Killeen 1991, Schimmelpenning 1992, Pallis 1992). Compared tonon-comedo DC IS, the comedo type much more often exhibits an increasedexpression of c-erbB-2 oncogene, which has been associated with badprognosis in invasive breast carcinoma (van de Vijver 1988, Bartkova 1990).Immunostaining of estrogen receptors is much more often negative in thecomedo type than in other, better differentiated DCIS types (Bur 1992). Theexpression of mutant p53 protein, which is associated with genetic instability andtumor progression, is also much more often present in comedo type than in noncomedo types (Poller 1993).Comedo tumors have a higher growth rate than other types of DCIS.Proliferation rate can be analyzed by the thymidine labeling technique, whichidentifies cells in the S-phase. Different proliferation rates of DCIS with differenthistological characteristics were detected with this method (Meyer 1986):Cribriform/micropapillary types are slowly proliferating, the solid type is anintermediate entity, and the comedo type has high proliferative rates. Thehistologic type of DCIS is predictive not only of proliferative rate of DCIS itself,but also of the proliferative rate of the associated invasive carcinoma. Thethymidine labeling index of an associated invasive component is similar to theindex of DCIS in about 90% of cases.25Moreover, the expression of nm23, a metastasis suppressor gene, is oftennegative in comedo DCIS, while other types show positive staining (Royds1993). Microinvasion is commonly found in association with the comedo type(Patchefsky 1989) and is often multifocal.It has been demonstrated that the comedo type of DCIS have a highercapacity to recur or to progress into invasive cancer than other types of DCIS(Lagios 1989, Schwartz 1992). Therefore, the biological characteristicsassociated with comedo DCIS might be linked to the aggressive behavior ofDCIS.1.1.1 Image cytometry of ductal carcinoma in situAneuploid DNA profile was more commonly found in DCIS which lacks thecytological or architectural differentiation (Crissman 1990) and was related to theoverexpression of c-erbB-2 oncogene, a putative prognostic factor for breastcancer (Visscher 1991). The findings of Schimmelpenning, who foundaneuploidy in 61% of DCIS, were similar. Aneuploidy was most common incomedo type (90%) and was related to the high nuclear grade and to the c-erbB2 overexpression.Other authors, who used flow cytometry (FC), also reported significantcorrelation between aneuploidy and high nuclear grade of DCIS (Killen 1991).FC of embedded tissue was used in another study and the relationship betweenhistological type of DCIS, DNA content and S-phase fraction was analyzed(Locker 1990). In this study the cribriform type appeared to be rarely aneuploid,26had a low proliferative rate and was usually low grade in contrast to comedo,solid, micropapillary or mixed types of DCIS.Differences in the DNA ploidy between different histological types havealso been reported by other authors: The proportion of aneuploid tumorsestimated on small samples with mostly 17 cases included in a single group was38-50% in cribriform type, 52-71 % in micropapillary type, 70-80% in solid typeand 82-91%% in comedo type DCIS (Crissman 1990, Fisher 1992,Schimmelpenning 1992, Pallis 1992). The overall frequency of aneuploid DCIScases appears to be between 60% and 70%, but is clearly much higher incomedo type than in the other types.Since aneuploidy in tumors most often marks a worse prognosis, it islikely that the same could be true for carcinoma in situ. With the use of theimage analysis of Feulgen-stained nuclei, aneuploidy was detected much moreoften in DCIS accompanied with microinvasion than in DCIS without any invasivefoci (Carpenter I 987a). Moreover, an association has been indicated betweenaneuploidy of DCIS and the recurrence after excision biopsy. Similarly,aneuploidy has been more frequently found in DCIS associated with invasionthan in DCIS alone in a study performed on tissue sections (Carpenter 1987b).In contrast to these results, no differences in the DNA ploidy were foundbetween pure DCIS and DCIS with an invasive component in the studyperformed by Fisher (Fisher 1992). In the above study no differences in theDNA ploidy could be demonstrated between comedo and non-comedo types.The reason for this surprising result might be that the comedo type wasdiagnosed only on the basis of intraluminal necrosis, while the grade of nucleiwas not taken into account.271.2. PROPOSALI .2.A Measurements of DNA content: Comparison of differentcytometry techniquesDifferent cytometry techniques were compared in order to establish thevalue of the DNA content measurements performed with image cytometry ontissue sections. The purpose was to examine if the nuclear DNA contentobtained by measurements on tissue sections can be applied to diagnosisand/or prognosis of breast diseases in addition to other nuclear features.Measurements of tissue sections have not been as widely used andaccepted for the purpose of DNA content measurements as other cytometrictechniques. However, image cytometry (IC) of tissue sections was chosen totest the hypothesis that nuclear features can offer a useful prognosticinformation in various breast diseases. With the use of IC it is possible to obtaininformation covering a wide spectrum of morphological characteristics of nuclei.With IC measurements performed on sections it is possible to visualize tissuehistology, to precisely localize the affected epithelium, and to selectively collectthe images of nuclei. With IC of tissue sections a lesion limited to a single ductcan be analyzed. This is important for the analysis of benign breast diseasesand DCIS where areas of affected tissue are often small or scattered throughoutthe section with a variety of disease patterns being present on the same section.The performance of different cytometric techniques was compared in theDNA measurements on archival specimens of invasive breast carcinoma. Thefirst question was, how do the FC measurements of the DNA content relate tothe IC DNA measurements performed on tissue sections from the same tissue28blocks. Dl and ploidy acquired by FC and IC from the adjacent tissue sectionswere compared.The effect of different preparative and sampling methods on the DNAcontent measurements of breast carcinoma was also studied. IC measurementswere performed on: i) archival smears of breast aspirates, ii) tissue sections fromembedded tissue blocks, and iii) disagreggated nuclei from corresponding tissueblocks. The DNA ploidy and DNA histogram parameters, obtained by the threetechniques were then analyzed.I .2.B The relationship between the histological patterns of breastdiseases and quantitative nuclear featuresQuantitative nuclear features reflect morphologic nuclear changes. It maybe expected that changes in quantitative features correspond to increasingstructural and functional DNA abnormalities, which are associated with thedevelopment of malignant tumors. Nuclear features of various categories ofbreast diseases were studied in order to demonstrate how nuclear featurescorrespond to human classification of breast diseases. Quantitative featurescould be possibly used as an aid for a more objective diagnosis of thesediseases.The aim was to characterize nuclear morphology of various breastdiseases in a quantitative way with the use of nuclear features measured byimage cytometry. Nuclear features were analyzed in the following groups ofbreast diseases: Normal breast tissue, non-proliferative breast disease,proliferative breast disease, DCIS, and invasive carcinoma. It was expected that29certain features, such as nuclear area or shape, would change progressively inrelation with advancing histological changes and with an increasing degree ofproliferation and atypia.I .2.C Nuclear features as a prognostic factor for DCISPrior to the study of the prognostic value of nuclear features for DC IS, themain histological types of DCIS were characterized on the basis of their nuclearfeatures. The differences between main types were analyzed for the DNAploidy, nuclear size, shape and chromatin distribution. The purpose of thisanalysis was to avoid any effects that these differences might have on the studyof the prognostic value of nuclear features.•The importance of prognostic factors for DCIS is difficult to study becausethe lesion is usually completely removed in order to be successfully treated.Indirectly it might be possible to evaluate some prognostic factors with acomparison of i) DCIS without invasive cancer (DCISI) and ii) DCIS withsynchronous invasive cancer in the same breast (DCIS2).One of the objectives in the present study was to test the hypothesis thatnuclear features of DCIS2 differ from nuclear features of DCISI. The hypothesisassumed that some nuclear features, related to the DNA amount in the nucleus,nuclear size, shape, contour, chromatin texture or proliferation rate of cells, maybe more irregular when measured on DCIS with invasive cancer present in thesame breast. We proposed that DCISI and DCIS2 could be distinguished onthe basis of their nuclear features measured by image cytometry (Figure 5).The demonstration of differences between DCISI and DCIS2 would haveINVASIVECARCINOMANUCLEIFigure5.DUCTALCARCINOMAINsmi WITHOUTINVASIVECOMPONENT(DCIS1) ANDDUCTALCARCINOMAINSifUWrIHINVASIVECOMPONENT(DCIS2)....•.iINSITUNUCLEI:DIFFERENCESINNUCLEARMORPHOLOGYBE1WEENDCIS1ANDDCIS2CANBEDEMONSTRATEDWITHQUANTITATIVEANALYSISOFNUCLEARFEATURES.CARCINOMAINSITUWITHCARCINOMAINSITUASSOCLATEDINVASIVEWITHOUTINVASIVECOMPONENT(DCIS2)COMPONENT(DCIS1)31an important, clinically relevant implication: Nuclear features may be predictiveof the invasive potential of DCIS and may be possibly applied as markerspredictive of the subsequent behavior of DCIS tumors.I .2.D Malignancy associated changesThe hypothesis proposed that nuclear features of normal tissue in breastswith carcinoma differ from the nuclear features of normal tissue obtained frombreasts without a malignancy. Similar observations have been reportedpreviously as malignancy associated changes (MAC), but have not been yetdemonstrated in breast tissue.One of the objectives of this thesis was to demonstrate the existence of MAC inbreast tissue with the analysis of quantitative nuclear features of nuclei fromnormal appearing lobules in the vicinity of carcinoma. The comparison ofnuclear features of normal appearing tissues obtained from cancerous and non-cancerous breast could provide useful prognostic information. If differencescould be demonstrated, then the combination of selected quantitative featurescould be used as a marker to indicate the presence of malignancy in the breast.The demonstration of MAC on breast tissue could aid in the cases wheremalignancy is present in the breast but not found in the biopsy because of theinadequate sampling. The implications of such finding would also be importantfor studying the prognosis of benign breast disease: The presence of MAC in abenign breast biopsy may be indicative of progressive potential of the benigntissue and might be associated with an increased risk of the subsequentinvasive carcinoma (Figure 6).MALIGNANCYASSOCIATEDCHANGES(MAC)ENON-CANCERSUBJëiPATIENTWITHEARLYCANCERIFigure6.MAC:MALIGNANCYASSOCIATEDCHANGES,Normalcellsintheproximyofmalignancy(andapie-malignantlesion?)haveslightlydifferentnucleartexturefeaturesthanthecellsfromanon-cancersubject.DIAGNOSTIC332. OBJECTIVESTHE OBJECTIVE OF THIS THESIS WAS TO INVESTIGATE THE HYPOTHESIS THATQUANTITATIVE NUCLEAR FEATURES CAN PROVIDE PROGNOSTICINFORMATION FOR BENIGN BREAST DISEASE AND DUCTALCARCINOMA IN SITU. THE FOLLOWING ISSUES WERE STUDIED:1. THE VALUE OF DNA MEASUREMENTS PERFORMED BY IMAGE CYTOMETRY ONTISSUE SECTIONS IN RELATION TO THE DNA MEASUREMENTS PERFORMED BYFLOW CYTOMETRY OR AUTOMATED IMAGE CYTOMETRY TECHNIQUES;2. THE RELATIONSHIP BETWEEN QUANTITATIVE NUCLEAR FEATURES ANDADVANCING MORPHOLOGICAL CHANGES OF BREAST TISSUE;3. QUANTITATIVE NUCLEAR FEATURES OF VARIOUS HISTOLOGICAL TYPES OF DCIS;4. DIFFERENCES IN NUCLEAR MORPHOLOGY BETWEEN PURE DCIS AND DCISASSOCIATED WITH SYNCHRONOUS INVASIVE CARCINOMA;5. MALIGNANCY ASSOCIATED CHANGES: SUBTLE CHANGES OF NUCLEARMORPHOLOGY IN THE EPITHELIAL CELLS OF NORMAL LOBULES IN THE VICINITY OFINVASIVE CARCINOMA.343. MATERIALS AND METHODS3.1 TISSUESOne pathologist (dr. Ann Worth) reviewed the diagnoses of all the slidesand of all the selected areas on each slide.3.1 .A Comparison of image and flow cytometryForty-eight formaldehyde fixed paraffin embedded tissue blocks wereavailable for the study. The blocks were one to ten years old. The presence oftumor was confirmed on haematoxylin-eosin (HE) stained sections. After thefirst HE section, two sections were cut for FC, followed by two serial 4 tmtissue sections for IC and for another HE slide. The majority of cases werediagnosed as invasive ductal cancers and many had a coexisting ductalcarcinoma in situ (DCIS). One case was diagnosed as colloid carcinoma andanother case as medullary carcinoma. In six cases only DCIS was present inthe tissue block.3.1 . B Comparison of image cytometry measurements performedon smears, cytospins and sectionsArchival smears from breast aspirates, and formaldehyde-fixed, paraffinembedded tissue blocks from subsequent biopsy or mastectomy, were availablefor seven patients with invasive breast carcinoma. Three adjacent tissue35sections were cut from each tissue block. The first section was stained withthe routine haematoxylin-eosin stain in order to confirm the diagnosis. Thesecond 4m section was used for the image cytometry measurements, and thethird section, 5Opm thick, was used to prepare the cytospin of the nuclearsuspension for the image cytometry measurements. All of the patients hadinvasive breast carcinoma. The diagnosis of smears was in agreement with thehistological diagnosis in all cases except one, where the smear was negative formalignancy due to the lack of the diagnostic cells.3.1 .C Correlation of quantitative features with advancingmorphological changesThe paraffin embedded tissues used for image analysis originated fromfine wire biopsies taken as a result of positive mammography screening or frommastectomy material. Tissues of patients with the diagnosis of benign breastdisease, DCIS or invasive carcinoma were analyzed. The number of caseswhich were analyzed is shown in Table 2.Measurements were performed on following tissues: i) areas of normaltissue from breasts with a variety of diseases, ii) non-proliferative disease, iii)proliferative breast disease, iv) non-comedo DCIS, v) comedo DCIS, and vi)invasive ductal carcinoma.Images of normal nuclei were collected from normal breast lobules ofpatients with benign or malignant breast disease. Normal breast lobule is shownin Figure 7.36Table 2. THE NUMBER OF CASES ANALYZED IN SIX GROUPS OF BREAST DISEASES.DIAGNOSIS OF THE ANALYZED AREA NUMBER OF CASESNORMAL TISSUE 53NON-PROLIFERATIVE DISEASE 8PROLIFERATIVE DISEASE 18NON-COMEDO ocis 60COMEDODCIS 21INVASIVE DUCTAL CARCINOMA 37Proliferative diseases included moderate or severe ductal hyperplasia,scierosing adenosis and papi I loma. Non-proliferative disease groupconsisted of mild ductal hyperplasia. Mild hyperplasia is truly aproliferative disorder. However, in the classification of breast diseasesmild hyperplasia is included in the group of non-proliferative diseases.37.4Figure 7. NORMAL BREAST LOBULE.Haematoxylin-eosin stain. Magnification on the print is approximately 172:1:(objective 16 X wide-field system factor 0.63 X optovar factor 1.25 X projective3.2 X magnification of the print 4.3 = 172)e‘ W%aaA..S -.0a —$/‘IF38For the cases included in the non-proliferative disease group,measurements were performed only on areas with mild hyperplastic changes(Figure 8). Mild hyperplasia is truly a proliferative disorder. However, in theclassification of breast diseases mild hyperplasia is included in the group of non-proliferative diseases because it is not associated with an increased risk ofsubsequent carcinoma as are the other entities in the group of proliferativebreast disease.Changes analyzed in proliferative disease group included moderate andsevere ductal hyperplasia, sclerosing adenosis and papilloma. Proliferativelesions showed various degrees of nuclear and/or architectural atypia.However, due to a small number of analyzed cases in this group, they were notfurther divided according to the degree of atypia. Figure 9 and 10 showexamples of proliferative breast disease.DCIS consisted of 21 comedo and 60 non-comedo cases. For thepurpose of this study, criteria for the diagnosis of comedo type consisted of: i)ducts distended by a confluent proliferation of CIS cells, ii) intraluminal necrosis,iii) and a high nuclear grade with the majority of cells having grade 3 nuclei.Periductal fibrosis and inflammation were found in most comedo cases includedin this study. Figure 11 shows an example of comedo DCIS.Invasive carcinoma group consisted of 37 cases of invasive ductalcarcinoma (Figure 12).One pathologist reviewed the diagnosis of the slides, as well as allselected areas on each slide.39Figure 8. NON-PROLIFERATIVE BREAST DISEASE: MILD HYPERPLASIA.Haematoxylin-eosin stain. Magnification on the print is approximately 172:1.40Figure 9. PROLIFERATIVE BREAST DISEASE (A): SCLEROSING ADENOSIS.Haematoxylin-eosin stain. Magnification on the print is approximately 69:1.41Figure 10. PROLIFERATIVE BREAST DISEASE (B): MODERATE DUCTAL HYPERPLASIA.Haematoxylin-eosin stain. MagnifiCation on the print is approximately 107:1.Figure 11. DUCTAL CARCINOMA IN SITU: COMEDO TYPE.Haematoxylin-.eosin stain. Magnification on the print is approximately 107:1.42.:• I - -•,,,._......%.•_•-.a’•.i••• 1. .• -• • -• l%. -: -. / .‘I. • I ‘-• • -•.c__ -)r?.’ :43-i-•ç•. • •‘•._.‘.-‘‘••- • ..‘ I —• • -•‘.: :•.••4Figure 12. INVASIVE DUCTAL CARCINOMA.Haematoxylin-eosin stain. Magnification on the print is approximately 69:1.—14 .-443.1.D Heterogeneity of DCISDifferences in quantitative nuclear features were studied betweencomedo DCIS (21 cases) and non-comedo DCIS (60 cases). Also, comparisonof nuclear features was made between various histological types of non-comedoDC IS. The analysis included cribriform (II cases), papillary (5 cases),confluentlacinar (24 cases), mixed (13 cases), and non-specific types (7 cases).In mixed type the combinations of two or more non-comedo types were present.Non-specific type consisted of non-comedo DCIS with architectural patterns thatdid not fit to any of above categories.3.1 . E Differences between pure ductal carcinoma in situ andductal carcinoma in situ associated with invasive carcinomain the surrounding tissueTo test for differences in quantitative nuclear features between pureductal carcinoma in situ (DCIS) and DCIS with synchronous invasive carcinoma,specimens containing DCIS were obtained from patients with or without invasivecarcinoma of the breast. Comedo and non-comedo types of DCIS were studiedseparately. The numbers of cases involved are shown in Table 3.3.1 .F Malignancy associated changesThe aim was to compare normal breast epithelial nuclei originating frompatients with benign breast disease and normal epithelial nuclei from breast45Table 3. THE NUMBER OF PURE DCIS AND DCIS WITH ADJACENT INVASIVECARCINOMA INCLUDED IN THE STUDY.HISTOLOGICAL DCIS WITHOUT ASSOCIATED DCIS WITH ASSOCIATEDTYPE INVASIVE CARCINOMA INVASIVE CARCINOMACRIBRIFORM 6 5PAPILLARY 2 3CONFLUENTIACINAR 12 12MIXED 7 6NON-SPECIFIC 4 3NON-COMEDO 31 29COMEDO 6 15Cases included in the study of the differences in nuclear features betweenpure DCIS and DCIS associated with invasive carcinoma of the breast.Comedo and non-comedo type were studied separately. Differenthistological types of noncomedo DCIS were similarly distributed in bothgroups (DCIS without associated invasive carcinoma and DCIS withassociated invasive carcinoma). The majority of noncomedo cases hadnuclear grade I or II. Three cases of noncomedo DCIS had a high nucleargrade (Ill): two of them were associated with invasive carcinoma in thesurrounding breast.46carcinoma patients to see if malignancy associated changes could be detected.In all cases only nuclei designated as normal were selected for the study fromnormal lobules without any apparent changes (an example of normal lobule is inFigure 7).The benign group included 18 cases of non-proliferative disease with thediagnosis of mild fibrocystic disease, which included mild hyperplastic changes,cysts, fibrosis and duct ectasia. An additional 2 cases with proliferative diseasewere diagnosed as sclerosing adenosis and as mild to moderate hyperplasia.The malignant group included 23 cases of invasive carcinoma and 11cases of DCIS. Normal nuclei were selected from normal lobules from theneighborhood of carcinoma or DCIS. In most of the malignant cases normalnuclei were collected from the slides on which carcinoma was also present.However, in some cases the sections with carcinoma did not contain any normallobules and thus cells from the adjacent sections where no carcinoma waspresent were analyzed. Table 4 shows the numbers of cases and nucleianalyzed to detect MAC.3.2 STAINING3.2.A Flow cytometryFrom a section an area with invasive carcinoma was selected. Thetissue was cut out, dewaxed in xylol and rehydrated using a series of alcohols.The tissue was mechanically disaggregated and centrifuged at 400 rpm for 10minutes at 4°C. The pellet was resuspended in 0.5% pepsin in 0.9% sodiumchloride with the addition of 3% PEG 6000 at pH of 1.5. The disaggregated47Table 4. CASES INVOLVED IN THE ANALYSIS OF MALIGNANCY ASSOCIATED CHANGES.DIAGNOSIS NUMBER OF CASES (NUCLEI)BENIGN 20 (1931)DCIS 11 (577)INVASIVE CARCINOMA 23 (1188)Table shows the diagnosis and number of cases, where normal nucleiwere collected from normal areas of tissue for the purpose of MACanalysis. Benign group included 18 cases with a diagnosis ofnonproliferative disease, and 2 cases with a diagnosis of proliferativedisease.48tissue was then incubated in a water bath at 37°C for 30 minutes and vortexed at5-minute intervals. Samples were treated with Pepstatin A to arrest enzymaticactivity. The suspension was filtered through a 60gm mesh and nuclei werecentrifuged at 400 rpm for 10 minutes at 4°C. The pellet was resuspended inHanks/Hepes solution at 4°C. Nuclei were treated with 0.1% TritonXlOO in PBSfor 3 minutes at 4°C and incubated with RNA-ase for 20 minutes at 37°C. Thesamples were stained at 4°C with Propidium iodide ( in PBS) and storedat 4°C until the measurements were carried out. The measurements wereperformed using a Coulter Epics-C flow cytometer within 12 hours after staining.FC staining and measurements were performed by the Analytical CytologyLaboratory of the Pathology Department in the B.C. Cancer Agency.3.2.8 Image cytometryAlcohol fixed smears stained with the Papanicolaou method weredeposited in xylol for 48 hours and then the coverslips were removed. Slideswere washed in 100% ethanol, destained in acid alcohol (70 ml of 100% ethanol+ 30 ml of distilled water + I ml of concentrated HCI) for 30 minutes. To preparethe cytospins 50tm sections were cut from formaldehyde fixed, paraffinembedded tissue blocks. Sections were treated with the same disaggregationprocedure which was used in the preparation of nuclear suspensions for flowcytometry measurements (3.2.A). Nuclear suspensions (0.75 ml) werecentrifuged onto the slides with the Shandon cytospin centrifuge (900 rpm, 5minutes) and air-dried. Tissue sections, thick, were incubated in an ovenfor 10 minutes (50-60°C) and dewaxed in xylol (2X1 0 minutes).49The following staining procedure was the same for tissue sections,smears and cytospins. Slides were rehydrated in series of alcohols (5 minutesin 100% ethanol, 2X1 minute in 75% methanol, 2X1 minute in 50% ethanol and2X1 minute in water). Slides were post-fixed in Bohm-Sprenger fixative (640 mlmethanol + 120 ml formaldehyde + 40 ml glacial acetic acid) for 45 minutes,washed in water, and incubated in 5N HCI for 30 minutes. After hydrolysis theslides were washed in running water for 5 minutes, stained with Thionin-502andwashed again in running water for 5 minutes. This procedure providesstoichiometric staining of DNA and can therefore be used for quantitative DNAnuclear measurements (Mikel 1991, Tezcan 1994). The slides were thencounterstained with Orange G which binds cationic groups on the proteins (0.5gof Orange G in lOOmI of 50% ethanol) for 30 seconds (Oud 1984). Orange Gstains connective tissue and makes it easier to visualize histological features.DNA measurements are not appreciably affected by the use of this counterstain,since the absorption spectra of Thionin and Orange C are minimally overlapped.Sections were then washed in 100% alcohol, dehydrated in xylol for 5 minutesand mounted with Paramount.3.3 IMAGE CYTOMETRY MEASUREMENTS3.3.A Image cytometry of tissue sectionsThe image analysis device consisted of a Nikon Optiphot microscope witha IX video projection lens, 100W halogen light with a stabilized power supply,3-chip CCD video camera (Sony, DXC-3000), MVP-AT Matrox image processingboard and IBM compatible personal computer (Figure 13). The photometricRGBANALOGUEMONITORKEYBOARDMICROSCOPEFigure13.IMAGECYTOMETRYDEVICE.Thediagramshowsmajor componentsoftheimagecytometrysystem,whichwasusedforthemeasurementsperformedontissuesections.TLMONOCHROMEMONITOR3-CHIPCCDCAMERARGBif_____jpfiI••••....._]MOUSEU,C51resolution was 256 gray levels. Measurements were performed with Plan Apo60X objective (60/1.40, Oil) (Zbieranowski 1992). The pixel size of the camerawas I 3X1 3tm. This resulted in a pixel spacing on the specimen plane of Camera non-linear response was corrected for by calibrating measuredintensities against a known optical density reference. Non-uniformity of the fieldillumination was corrected for by subtraction of the image of a blank field. Thesecorrections have been described previously (Jaggi 1988, Mac Aulay 1989).From each section 50 lymphocytes and 50-200 epithelial nuclei wereselected for each diagnostic category. Their nuclear images were acquired intoseparate classes in the image file. Up to five different classes of cells could beselected from the same slide. On a section with invasive carcinoma for example,the nuclei of DCIS, nuclei of proliferative disease, normal nuclei andlymphocytes could be collected in addition to invasive cancer cells. Cells fromareas with different histological patterns were always collected as distinctclasses in the image file. Different histological types of DCIS were alsocollected as different classes in image file, whenever a sufficient number ofnuclei was available. Comedo and non-comedo nuclei were distinguished inevery case where they were present in the same section. The number ofselected cells depended on how many were present on the analyzed slide; atleast 50 and up to 200 nuclei were collected for each cell class. Multiple fieldswere scanned across the slide. Approximately ten nuclei were selected fromeach field. Only non-overlapping nuclei, that appeared to be in focus, wereselected.Nuclei were automatically segmented on the basis of the selectedthreshold and their images were stored in the computer memory (Mac Aulay521989). The process of segmentation refers to the separation of nuclei from thebackground. To delineate nuclei from the background, an appropriate opticalintensity threshold was chosen for different cell types by the observer. Thisthreshold separated nuclei from the lighter background. Nuclei with morecondensed chromatin, such as lymphocytes, needed a lower threshold to bedelineated from the surroundings. Manual correction of the nuclear contour wasapplied in the instances of touching, but not overlapping, nuclei.3.3.B Comparison of measurements performed on tissue sections,cytospins and smearsA different IC device was used for this part of the study than for the rest ofthe IC measurements because of its advantage that measurements can beperformed in both ways, automatically or manually. This system has beenrecently developed in our department for the purpose of automated cervicalscreening (Garner 1992), but it is currently also applied for measurements onvarious other tissues. The device consists of Microimager 1400 digital camera,automated stage and IBM-compatible computer. This system is optimized forquantitative measurements, and provides much higher spatial resolution than theconventional system (Jaggi 1990, Jaggi 1991, Palcic 1990).The measurements were performed at 25X magnification. The width of asingle pixel of this system is, corresponding to a spatial resolution of 0.27jim (6.8/25=0.27) at the specimen plane. Pixel refers to a picture element and isdefined as the area on the image which is covered by one sensing element ofthe detector.53From tissue sections 150-200 carcinoma nuclei and 50 lymphocyte nucleiwere selected and their images collected manually. On average, 15 nuclei werecollected from each microscopic field and 10-20 fields were scanned across thewhole section.Smears were scanned in an automated way. The focusing andsegmentation was entirely automated. The upper limit of the number of collectedcells was 1000. In smears with sparse cells all microscopic fields were scannedin an attempt to collect all cells on the slide. Cellular smears were scanned ina zigzag pattern at regular spacings over the surface of the entire microscopeslide. Cytospins were also scanned in an automated way.The machine was instructed to scan as many microscope fields asnecessary to collect 1000 cells. Images were reviewed on the screen andunacceptable images were discarded. Images were considered unacceptable ifthe nuclei were not in focus, if they contained more than one nucleus, or if theycontained non-nuclear objects, such as cellular debris. The selection ofunacceptable images was performed in a semi-automated way. For example, afew images representative of poorly focused cells were selected by the observer,and then all similar images were classified with the use of thresholds of selectedfeatures. Lymphocytes and granulocytes were classified into separate classesin a similar manner, and their mean value of integrated optical density (IOD) wasused to normalize the lCD of other nuclei. In two smears and in one cytospinno cell could be recognized as normal (lymphocytes, granulocytes, fibroblasts,myoepithelial cells) and the histogram was normalized against the mean value ofthe first peak.543.4. DESCRIPTION OF MAIN NUCLEAR FEATURESSixty-three features were calculated for each nucleus and stored in theform of feature files. The algorithms of the features have been definedpreviously (Mac Aulay 1989). Here the main nuclear features are described.The following features are representative of the feature classes and were foundto be more important than other features for the purpose of this study.Photometric features1) IOD (integrated optical density): Optical density is proportional to theamount of light which is absorbed by a stained object and hence thedensity of stain. Integrated optical density is the sum of the opticaldensity values of all object pixels and is proportional to the amount ofstain in the object. If the stain is a stoichiometric DNA stain, then IOD isproportional to the DNA amount in the nucleus.2) OD VAR (variation of optical density) represents the variation of theoptical density distribution over the pixels of an object. It is equal to thevariance of optical density values in the object, normalized by the squareof the mean optical density of this object.3) OD MAX (optical density maximum) is the largest optical density valuedetected inside the object.Area and shape features4) AREA (nuclear area) is defined as the number of pixels which composethe object image.555) MEAN RADIUS is the mean distance from the center to the edge of theobject.6) VAR RAD (variation of radius) is calculated as a variance of thedistances from the object center to the object edge.7) COMPACTNESS expresses the ratio between the perimeter and area ofthe object. It is an indicator of the shape irregularity.8) ELONGATION represents the ratio between the major to the minor axisof the object.9) BDYI (coarse boundary variation), and BDY2 (fine boundary variation)are the measure of the smoothness of the object’s contour.Continuous texture features10) C-MASS (center of mass) corresponds to the distance between thegeometrical center of the nucleus and the center of the mass of opticaldensities of all the pixels in a nucleus..11) C-MASSL (center of mass of the low density chromatin): the same as C-MASS but calculated only for the low density chromatin.12) ENERGY is related to the regularity of chromatin distribution. A largeenergy value indicates a high degree of organization in spatial or grayscale distribution of optical density values in the nucleus.13) ENTROPY is related to the irregularity of chromatin distribution. A largeentropy value indicates a low degree of organization in spatial or grayscale distribution of optical density values in the nucleus.14) CONTRAST is related to the number of neighboring pixels with diverseoptical density values. A nucleus with a large contrast has large gray56scale variations at high spatial frequencies.15) HOMOGENEITY: a large value indicates a gray level variation that isspatially smooth (it occurs at low spatial frequency).16) CLUSTER SHADE: a large value of this feature indicates that distinctclumps exist in the nucleus, with a large contrast between clumps and therest of the nucleus. A positive value indicates bright clumps on the darkbackground, and a negative value indicates dark clumps on the lightbackground.17) FAREAI (fractal area 1) and FAREA2 (fractal area 2): for the calculationof these two features, the optical density values are taken as the height ofthe pixels in three dimensional space. The area of the obtained surfaceis measured for both features but at different scales.Discrete texture features18) TARL (total area ratio for the low density chromatin) is the area occupiedwith the low density chromatin divided by the area of nucleus.19) TARM (total area ratio for the medium density chromatin)20) TARH (total area ratio for the high density chromatin)21) TERL (total extinction ratio for the low density chromatin) is theintegrated optical density of the low chromatin area divided by theintegrated optical density of the nucleus.22) TERM (total extinction ratio for the medium density chromatin)23) TERH (total extinction ratio for the high density chromatin)24) ADL (average distance of the low density chromatin from the nuclearcenter) is the average distance between individual light pixels and center57of the nucleus, normalized by the mean radius.25) ADM (average distance of the medium density chromatin from thenuclear center)26) ADH (average distance of the high density chromatin from the nuclearcenter)27) MAER (medium average extinction ratio) is the ratio of mean opticaldensity of medium density chromatin and the mean optical density of thelow density chromatin.28) MHAER (high average extinction ratio) is the ratio of mean optical densityof medium/high density chromatin and the mean optical density of the lowdensity chromatin.29) NL (number of low density chromatin clusters) is the number of distinctgroups of low density chromatin pixels in the nucleus.30) NM (number of medium density chromatin clusters)31) NH (number of high density chromatin clusters)32) CRL (low density chromatin compactness ratio) is the ratio ofcompactness of the low density chromatin areas to the compactness ofhigh and medium density areas.33) CRH (high density chromatin compactness ratio) is the ratio ofcompactness of the high density chromatin areas to the compactness oflow and medium density areas.583.5 ANALYSIS OF DNA HISTOGRAMS3.5.A Image cytometryFeatures could be displayed in the form of histograms, separately foreach class of nuclei in the image file. The lOD of nuclei was normalized againstthe mean lOD of lymphocytes from the same slide (cells with normal DNAcontent). IOD histograms were plotted with 64 bins to span the region on the Xaxis from n=0 to n=5. Normalized IOD histograms were then analyzed by thedetermination of the following conventionally accepted parameters: DNA index(Dl) of the peaks, coefficient of variation of the peaks, entropy of the DNAhistogram, 1.25 exceeding rate (percentage of cells with IOD higher thann=1 .25), and 2.5 exceeding rate (percentage of cells with IOD higher thann=2.5). These limits for exceeding rates were used previously because they arerelated to the proliferation of tissue and to the proportion of aneuploid cells:Cells with DNA content higher than 1.25 could be aneuploid cells or proliferatingdiploid cells, and cells with DNA content higher than 2.5 are aneuploid cells(Fallenius 1988). Entropy of the DNA histogram was calculated as previouslydescribed (Stenkvist 1990). The Dl refers to the modal value of therepresentative peak (or peaks) in the histogram. Separate peaks with more than20% of cells were recognized as representative peaks. Peaks were identified asaneuploid if their Dl was higher than 1.2. This limit covered the range of twostandard deviations of the normal epithelial cells distribution. In some instancesthe mode of a peak was less than 1.2, but the distribution of a peak was verywide and the majority of values was spread beyond 1.2. These peaks were alsoidentified as aneuploid. (Because of the relatively low resolution of the IC59histograms no tetraploid peaks were defined in this study). An example of aDNA histogram with histogram parameters is shown in Figure 14. Whenever thehistograms showed a single diploid peak the tissue was designated to be diploid.Diploid cells by definition should have normal DNA content - however, it isobvious that slight deviations from euploidy cannot be detected with theprecision of IC techniques. In most of the malignant tissues, assigned as diploidin this study, the DNA changes were probably too small to be detected by IC, butcould be demonstrated with other techniques such as molecular genetics.3.5.B Flow cytometryTo obtain comparable plots for the comparison of image cytometry (IC)and flow cytometry (FC) measurements, the raw features obtained by bothtechniques were plotted to histograms with the use of the same program. FChistograms had 200 channels, and IC histograms had 50 channels to span from0 to 5, where the value I was equivalent to the diploid DNA content. IChistograms were analyzed as described in the previous paragraph.Flow histograms were normalized against the modal value of the firstpeak in the histogram, assuming that this peak contained diploid cells. The DNAindex (DI) was determined from the normalized histograms as the mode ofrepresentative peaks. A peak in FC histograms had to contain more than 10 %of nuclei in order to be recognized as a representative peak. The DNA ploidywas determined for the histograms according to the presence of aneuploidpeaks. For the classification of peaks the limits were set on the DI value. In the60DCS:DI=1. 525 IQD=1. 60ENTROPYO. 641. 25 EXCEEDING RATE=85%2. 5 EXCEEDING RATL=3%2015za10-I r’///-I______LYMPHOCYTES023 5NORMALIZED CDFigure 14. AN EXAMPLE OF NORMALIZED HISTOGRAM AND HISTOGRAM PARAMETERS.Histograms of integrated optical density (IOD) are shown for comedo carcinomain situ and for lymphocytes (internal control cells). DNA index of DCIS wasdetermined as the mode of the peak. IOD was normalized by dividing the IOD ofcells of interest with the IOD of control cells (lymphocytes). Mean lCD wascalculated by dividing the sum of the integrated optical densities of all nuclei withthe number of analyzed nuclei. Two exceeding rates were calculated as thepercentage of nuclei with their lCD higher than the chosen limit (1.25- and 2.5-exceed ing rates).61FC histograms a single peak was always classified as diploid. The second peakwas recognized as aneuploid if its Dl was higher than 1.1. Tetraploid peakswere not defined but were assigned as aneploid for the purpose of this study.3.6 STATISTICSThe statistical analysis procedures involved non-parametric tests and Ttests (to test the differences between the means of two populations anddetermine if the differences are statistically significant) and the discriminantfunction analysis (to produce a classification of patients based on nuclearfeatures analysis). The analysis was carried out with the BMDP statisticalpackage (BMDP, 1988). The analysis of cervical tissues has shown previously,that the distribution of the majority of the nuclear features is not normal (MacAulay 1989). This has been confirmed on our preliminary data set when thenormality of feature distributions was tested. T-tests are valuable only whenvariables are normally distributed. Since non-parametric tests can be used forvariables with the distribution other than normal, both types of tests were usedtogether. Two variables were considered different when T-test and non-parametric (Mann-Whitney) test showed a significant difference (p<O.05). Thep-value of the non-parametric test is shown in all the tables.The 7M program of the BMDP statistical package was used for the lineardiscriminant function analysis with stepwise variable selection. This multivariatestatistical method is used to develop a classification rule (based on observationsmade on known groups of objects) that will assign a new object into one of thepossible groups on the basis of measurements made on the object. When a62large number of features is involved in the analysis, usually only the mostdiscriminating features are selected. With the selection of features we caninvestigate the causes of the differences between the groups. In addition, theclassification can sometimes be improved when irrelevant features are removed.The features are selected on the basis of their ability to discriminate betweengroups, which is determined by the F-value calculated for each feature. One ofthe methods that can be used for the selection of features is a stepwise forwardprocedure. With this method, a single most discriminating feature (with thehighest F-value) is initially found. Then the second feature is selected whichpaired with the previous feature offers the best discrimination. In the same waythe third variable, most discriminating in the combination with the previous twovariables, is chosen. This continues until a chosen n number of features isselected. The size of the selected feature set in the analysis is usually limited tothe number, above which adding new features to the discriminant function doesnot improve the classification in an important way. Possible bias and instabilitiesof the discriminant function analysis can be revealed using a jackknifedprocedure to generate classification matrices. The jackknife classification matrixis produced in the following way: One object is left out of the analysis and thenthe calculated discriminant function coefficients are applied on the features ofthis object to classify it to one or the other group. This is then repeated for everyobject. The classification matrix, obtained in this manner, indicates how well thediscriminant function would perform on a new data set.Two approaches were used in the discriminant function analysis:Discrimination on a slide by slide basis, and discrimination on a cell by cellbasis. Discrimination on a slide by slide basis provides classification of cases63(patients), based on the mean values and variances of features of nuclei from anindividual slide.With the second approach, all nuclei from all cases are gathered into thediagnostic groups and the feature values of individual nuclei are analyzed withthe discriminant function. This results in a classification of individual nuclei toone of the diagnostic groups. The coefficients of discriminant functionperformed on a cell by cell basis can be then applied to the feature files ofindividual cases to classify the same nuclei. In this way, we can obtain theproportion of correctly classified nuclei in each case (slide). The proportion ofcorrectly classified nuclei on slides can be then used to classify the slides(Figure 15).STATISTICS:PATIENTS:SLIDESDIAGNOSEDBYPATHOLOGISTCLASSIFICATIONOFSLIDES(PATIENTS)BASEDONNUCLEARFEATURES+++NDISCRIMINANTFUNCTIONANALYSISBASDONNUCLEARFEATURESCLASSIFIESNUCLEITO-0R,...1PROPORTIONOF-OR+NUCLEIONSLIDESFigure15.STATiSTICALANALYSISBASEDONCELLBYCELLDISCRIMINA11ONWASUSEDFORTHECLASIFICATIONOFPATIENTS.654. RESULTS4.1 THE DISTRIBUTION OF VALUES IN IMAGE CYTOMETRY DNAHISTOGRAMS OF NORMAL CELLSNormal epithelial cells or lymphocytes can be used as internal controlcells with the DNA content in the diploid range. Lymphocytes were chosen asthe internal control cells in our case, because they were always present insufficient numbers (at least 30) on the tissue sections. On the contrary, normalepithelial cells were absent in many cases. The cells of interest werenormalized by dividing their integrated optical density (IOD) by the IOD oflymphocytes. IOD equals the integrated optical density of a nucleus andcorresponds to the nuclear DNA content.Table 5 shows the DNA index (Dl) in histograms of normal cells. DI isdefined as the ratio between the modal value of the peak and IOD oflymphocytes. Even if discrepancies caused by differential staining of variouscell types might exist, they do not show an effect in histograms with a lowresolution, such as those obtained by the measurements of tissue sections.In addition to the DI, Table 5 shows the IOD, the coefficient of variation ofthe peak (CV), the entropy of the histogram, and the 1.25-exceeding rate for 20histograms of normal cells. These histogram parameters were calculated todemonstrate the limits of “normal” in the DNA histograms. The limits which wereused to distinguish diploid and aneuploid peaks were determined from thedistribution of DI values obtained from histograms of normal cells (1.0+1-0.2).CD-k0) I,(J’CDCDXCCDCD.cnCD-C’,CDQ-DCl)0)zh0)0-I0)0CDz(I)-I0C)-om-ImCl)0wzm‘10-lImzIC’) -0G)C,)0‘1z0Im-U-IImII-C) mIICo-0h-0-------0000---0---bobk,k,boeo0 -ø-or)ewcoc)a)r%)c)Ioc,Jco‘-‘ -C’,0zC’,PPP-ooo--bbbbobLni0X--%------—---.---..h••-00C)C0OC’)0)C)0b3-‘0I0)0000000000000000oooo-j.000-U.-<-.00CiC’,Cl’99674.2 COMPARISON OF FLOW CYTOMETRY (FC) AND IMAGECYTOMETRY OF TISSUE SECTIONS (IC)Examples of some IC and FC aneuploid and diploid histograms arepresented in Figure 16. The average coefficient of variation (CV) of diploid FCpeaks was 5%, while the average CV of the IC lymphocyte control peaks was10%. The average CV of normal epithelial cells in IC histograms was 11 %. AllIC diploid peaks and some of the aneuploid tumor peaks were well confined andeasy to interpret. However, some of the tumor peaks which were clearlyaneuploid had a wide distribution of values resulting in a large CV.Figure 17 shows the relationship between Dl as determined by FC and ICon the fifty-one tissue blocks. When more than one Dl was obtained by a singlemethod the more irregular Dl was used in the graph. When both invasive and insitu cancer had aneuploid peaks in the IC histograms, then DI of the invasivecancer was plotted. In about two thirds of the cases the IC values roughlycorresponded to the FC values. Most IC values were lower than correspondingFC values. In the remaining cases IC and FC completely disagreed on the Dl.Correlation between flow and image DNA index of cases which agreed on theploidy pattern was relatively low (r = 0.69).Agreement between the two techniques in ploidy determination wasreached in 79% (40/51) of all tissue blocks (Table 6) and/or 77% (36/47) oftumors. Both techniques confirmed aneuploidy in 62% of tumors (29/47) anddiploidy in 15% of tumors (7/47). If FC was considered to be a standard, ICFigure 16 a, b, c, d, and e. EXAMPLES OF DIPLOID AND ANEUPLOID IC AND FCHISTOGRAMS OF THREE CARCINOMA CASES.(A) IC and FC histograms have peaks in the diploid range, (B) both histogramsare aneuploid, (C) both histograms are aneuploid, but IC demonstrates anadditional population of cancer cells with a peak in the diploid range, while theproportion of carcinoma cells in the FC diploid peak is not known.A1CM68AFCM12345 12 345COUNT4030201002418126032241680BFCMB1CMjCOUNT4000300020001000024001800120060001200900600300045123C1CMIA12345CFCMLA12345 1234569• A ANEUPLOIDBYICANDFC• DIPLOID BY IC AND ECD ANEUPLOID ONLY BY FCV ANEUPLOIDONLYBYICAA><zzC)AA3. 02. 52. 01. 51.00. 5A9VV AkVAV AAAIDGDA AAAAAAAA AAAAA AA AA1.0 1.5 2.0 2.5 3.0FC DNA INDEXFigure 17. COMPARISON OF FLOW AND IMAGE CYTOMETRY ON THE BASIS OF THE DNAINDEX.Measurements were performed on 51 tissue blocks from 47 tumors. Roughly, intwo thirds of tissue blocks, the DNA indices obtained with image cytometrymeasurements corresponded to the DNA indices obtained with flow cytometrymeasurements.70Table 6. DNA ploidy as determined by IC and FC of 51 tissue blocks.FLOW CYTOMETRY__________Diploid Aneuploid totalIMAGE Diploid 7 5 12CYTOMETRY Aneuploid 6 33 35total 13 38 51There was an agreement in 79% (40/51) of cases: 87% (33/38) of FC aneuploidcases were also aneuploid by IC, while only about one half (7/13) of FC diploidcases were also found diploid by IC. The agreement was low in diploid cases.71agreed with FC on almost 90% of aneuploid cases, but on only about 50% ofdiploid cases.Six tumors were recognized as aneuploid only by IC. In five casesaneuploidy was detected only by FC. Table 7 shows the DI of the eleven caseswhere FC and IC did not agree in ploidy. Three of these cases (shown in thelast three rows in Table 2) had a diploid IC histogram and a FC histogram withDI values just slightly above the artificially set limit of 1.1. They were thereforeclassified as aneuploid by FC.For three tumors more than one tissue block was analyzed (Table 8). Inall tumors there was an agreement in the ploidy between the two methods. Bothmethods demonstrated a tumor heterogeneity in different tissue blocks. Inaddition, IC showed differences between invasive cancer and DCIS.Figure 18 presents the relationship between FC and IC DNA indices ofinvasive cancer and DCIS. In many of these cases DCIS nuclei seemed tobelong to a different population than invasive cancer cells. In some FChistograms invasive cancer was masked by the abundant DCIS. Overall,aneuploid histograms were observed in 71 % (22/31) of DCIS analyzed by IC.4.3 COMPARISON OF IMAGE CYTOMETRY MEASUREMENTSPERFORMED ON SMEARS, CYTOSPINS, AND SECTIONSFigure 19 shows scatter plots (area versus IOD) of a smear and acorresponding cytospin, and a tissue section originating from the same tumor.Nuclei of a smear and a tissue section are shown in Figure 20. Figures 21-22show DNA histograms of 6 other carcinoma cases where a smear, a cytospin72Table 7. Dl analysis of cases where FC and IC disagreed in ploidy.______ICDI Ploidy DI Ploidy1.00 D 2.012.3* A1.00 D 2.0 A1.00 D 1.7 A1.00 D 0.8I1.6* A1.09 D 1.511.6* A1.00 D 1.3 A1.87 A 0.9 D1.23 A 0.910.9* D1.17 A 1.0 D1.13 A 0.9 D1.12 A 1.111.0* DValues indicated by *denote the DI values of DCIS. The cases in the last threerows had Dl found by FC in the near-diploid area, just exceeding the artificiallyset limit of 1.1. They were therefore classified as aneuploid by FC. In theremaining cases with the discordant DNA ploidy outcome clear differences in Dlwere obvious: FC failed to detect 6 and IC failed to detect 2 aneuploid cases.73Table 8. HETERoGENEITY OF TUMORS.(Dl analysis of seven tissue blocks derived from three tumors.)PATIENT TISSUE FLOW IMAGE CYTOMETRYBLOCK CYTOMETRY INVASIVE DCISI A 2.00 - 1.0I1.9I B 2.05 - 2.0I C 1.93 2.0 1.1 12.02 A 2.13 1.1 l.112.02 B 1.1711.29 - I.112.33 A 2.20 1.2 1.13 B 1.88 - l.0Il.8DNA indices of seven tissue blocks derived from three tumors with image andflow cytometry measurements. In four tissue blocks only DCIS was present.Often two populations (two peaks) were present in DCIS. The differencesbetween different tissue blocks of the same tumor are shown by both methods.In addition, image cytometry demonstrates the differences between invasive andin situ carcinoma.• INVASIVECISpP •0-•742. 5____><wzz1.5 p .U.1.0,-.1.0 1.5IC DNA INDEXFigure 18. DNA INDEX OF INVASIVE CARCINOMA AND DUCTAL CARCINOMA IN SITU.In tissues, where ductal carcinoma in situ was present in addition to invasivecarcinoma, these were analyzed separately by image cytometry. Figure showsDNA indices of invasive and in situ carcinoma, measured by image cytometry,compared to a single DNA index measured by flow cytometry.2. 0 2. 5A 012345100001234500SECTION3Figure19. NUCLEARAREAVS.100SCATTERPLOTSOFASMEAR,ANDACORRESPONDINGCYTOSPINANDTISSUESECTION.PlotsshowtherelationshipbetweennormalizedIODandnuclearareaacquiredbyimagecytometryof abreastaspirate,cytospinofdisaggregatednucleiandtissuesection,allobtainedfromthesamebreastcarcinoma.Differencesinsamplingarerepresentedbythevaryingproportionofaneuploidcells(onlytumorcellimageswerecollectedfromtissuesection).Thedifferencesinthenuclearsizeofaneuploidpopulationsfromsmear,cytospinandsectionmaybetheresultofthedisaggregationprocedureanddifferentfixatives.SMEAR3B4000300020001 000CYTOSPIN3C400030002000410004 4200015001000500 0% Y.,•‘..:234100S(ii76ABFigure 20. MICROGRAPH OF NUCLEI ON A SMEAR AND ON A TISSUE SECTION.Figure shows a photograph of the Feulgen stained nuclei on a smear (A), and ona tissue section (B). Haematoxylin-eosin stain. Magnification on the print isapproximately 430:1.77>-C-)zUJEDaUJULizEDaUU-12345IOD12345IODSECTION 4II12345ODFigure 21. DNA HISTOGRAMS OF CORRESPONDING SMEARS, CYTOSPINS AND TISSUESECTIONS (A).DNA histograms obtained by automated collection of images from smears andcytospins of disaggregated nuclei correspond to the DNA histograms obtainedby manual collection of nuclear images from tissue sections. The figure showshistograms of carcinoma cases 1, 2, and 4.SMEAR 1 CYTOSPIN 1 SECTION 1I I1 2345CDSMEAR 21 2345CYTOSPIN 20. 330. 220. 110. CC0. 090. 060. 030. 000. 1 5n0. 050. 00OD45SECTION 20. 40. 30. 20. 10. 00. 1 60. 1 20. 080. 040. 000. 1 20. 080. 040. 000. 1 20. 090. 060. 030. 000. 1 20. 090. 060. 030. 000. 1 20. 090. 060. 030. 00123412345CDSMEAR 4I.C-)U-ED0UU..12345CYTOSPIN 4IOU78Figure 22. DNA HISTOGRAMS OF CORRESPONDING SMEARS, CYTOSPINS ANDTISSUE SECTIONS (B).DNA histograms obtained by automated collection of images from smears andcytospins of disaggregated nuclei correspond to the DNA histograms obtainedby manual collection of nuclear images from tissue sections. The figure showshistograms of carcinoma cases 5, 6, and 7.SMEAR 5 SECTION 5123 5005UzLJEDaLJLzUJEDaU]U]>-C)zLU]ED0LUU-0. 450. 300. 1 50. 000. 330. 220. 110. 000. 240. 1 60. 080. 0045SMEAR 60. 1 50. 1 00. 050. 000. 360. 240. 1 20. 000. 240. 1 60. 080. 00CYTOSPIN 5-4ODCYTOSPIN 61234IODCYTOSPIN 7J123ODSECTION 6J0. 200. 1 50. 1 00. 050. 000. 240. 1 80. 1 20. 060. 000. 1 20. 090. 060. 030. 005123400SMEAR 71 2345IODSECTION 71.12345 1 2345 1234500 OD OD79and a tissue section were measured for each case. All collected nuclei areincluded in the histograms of the cytospins and smears while only carcinomanuclei are plotted in the histograms of sections. Only in three cases (2, 3, and 4)do all three methods demonstrate aneuploidy, although in case 4 the peaks havedifferent positions. In the case 1, the cytospin failed to show an aneuploid peak(possibly because it contained a large number of inflammatory and necroticcells) and the smear and section disagreed in the position of the aneuploid peak.The histograms of the smears failed to show an aneuploid peak in the case 5and 6 (smear 6 was diagnosed as negative for malignancy). In these two casesthe cytospin and the section measurements disagreed in the position of theaneuploid peak. Case 7 is the only one where the histogram of the tissuesection contradicts both other methods by showing an aneuploid peak inaddition to a diploid peak.Table 9 compares the DNA histogram parameters of correspondingsmears, cytospins and tissue sections. Discrepancies are present in every case.The parameters of individual DNA histogram are in agreement with each other.When one of the parameters has a low value, the rest of them also have lowvalues. It appears that if IOD and Dl are known, entropy and exceeding rates donot add further information.4.4 CORRELATION OF QUANTITATIVE NUCLEAR FEATURES WITHADVANCING HISTOLOGICAL CHANGESIn many cases, areas with various histological changes were present inthe same tissue block or even on the same tissue section. By measuring nucleiC,)z0C.)Iii0wDC,)C,)IzU)z0U)0C.)000zz000w0C)LI00wIw0000a)...-.!‘4-00cG)050-‘Ca)OCDCCU.a)U) C.J.D -0A.1 •I.0x0a)a)E <°o*øEECCU)W0‘-C.).—C) •C.U) .. -.C. 0552ci) •— -Co.4-.-80>-a0 —coci—.CDCD1-00000zUi0cO)c0cOr-..I-cU)CDoob000c3o000d000000) cx0 00 c) U,;C)xr— c.j co 0 it, it) It)<QQ-v-’-°z ‘- ‘- ‘- ‘- ‘- ‘- ‘- . 0or-0cDIt)0a,0) Ø)O)LC)OQ0C)0Q,-‘-‘-‘- C1 () LI) CDZ’ Z z z z° z0 zUi zz zz r-Cl) QQQQQQO<OI—<OI-<OI-<O-<OI-<OI--<OI‘-)-WWW)-LLJ-W-W-WCl)QCOØOøø000Ci)Cl)OOØOCI)— — — — — — — — — — — — — — — — — — — — — —81of specific morphological changes, it was possible to observe the differences innuclear features of different breast diseases in the same patient. With the useof only two features in a scatter plot, it was possible to demonstrate the variationof different diseases occurring in the same patient.It has been claimed that various histological patterns represent steps inthe progression to invasive carcinoma. An example of an area vs. lOD scatterplot shows how the irregularities of nuclear size, DNA content or textureincrease with the .advancing morphological changes in an individual patient(Figure 23).The means of features and the standard deviations of the means werecalculated for all cases in six groups of breast diseases in order to analyze therelationships between quantitative nuclear features and advancingmorphological changes. A case here refers to a feature file, or feature files thatbelong to an individual patient and fit into one of the following six categories:Normal, non-proliferative disease, proliferative disease, non-comedo DCIS,comedo DCIS, or invasive carcinoma. (Each feature file carried the featureinformation for the nuclei collected from the areas of the slide with a specificdiagnosis. Hence, some individual patients included in this part of the studywere represented in more than one category, if their sections contained variousconditions). The number of cases included in different groups of diseases wasshown in Table 2.DNA histograms were analyzed to detect the differences in histogramparameters between six categories of breast diseases. Average values ofentropy and exceeding rates of all six groups are listed in Table 10. Theincrease in the entropy, 1.25-exceeding rate, and 2.5-exceeding rate can be82Figure 23. NUCLEAR AREA VS. IOD SCATTER PLOT OF VARIOUS HISTOLOGICALnaaCC Aa C0liiLii-JC-)z30002*00180012006000C4 C COMEDO DCISA MODERATE HYPERPLASIA+ATYPIA9 NORMAL LOBULES<.> LYMPHOCYTES0 I 2 3OD (a. u.)4 5PATTERNS PRESENT ON THE SAME SLIDE.83Table 10. DNA HISTOGRAM PARAMETERS IN SIX DIAGNOSTIC GROUPS.DIAGNOSIS OF THE NUMBER OF ENTROPY %>1 .25 %>2.5ANALYZED AREA CASES (STANDARD DEV.)NORMAL GLANDS 20 0.46 (0.04) 14 0NON-PROLIFERATIVE 8 0.53 (0.06) 35 0PROLIFERATIVE 18 0.52 (0.05) 30 0NON-COMEDO DCIS 60 0.58 (0.07) 40 3COMEDO DCIS 21 0.68 (0.07) 62 3.5INVASIVE CARCINOMA 30 0.63 (0.09) 58 9Table shows average values of entropy of the histogram, 1.25-exceedingrates, and 2.5-exceeding rates for the six categories of breast diseases.84seen from the lowest values in the normal group to the highest values in theinvasive carcinoma group and comedo DCIS group.In the group of benign breast diseases we detected aneuploidy in 2patients. The first patient had a diagnosis of “moderate hyperplasia with atypia”.Aneuploid peaks in this case were detected when nuclei were collected in thearea with moderate hyperplasia and also in the area with only mild hyperplasticchanges on the same slide. The second patient had a diagnosis of invasivecarcinoma. In addition to an aneuploid peak of invasive carcinoma, similaraneuploid peaks showed up in the histograms of nuclei collected from areas withmoderate, and mild hyperplastic changes. There was no DCIS present.The values of four features (100, area, variation of radius, variation ofoptical density values in the nucleus) and two variances (variance of 100,variance of area) are shown for six groups of diseases in Table 11. Theseparticular features were chosen for the table because each represents adifferent characteristic of a nucleus: DNA content, size, shape, and chromatintexture. The two variances provide different information, which is related to theinter-nuclear variation in DNA content, size, shape, and texture. Table 11 showsthat mean values of four features and their variances increase, from the lowestvalues in the normal group, to the highest values in the invasive carcinomagroup. (The exception is comedo DCIS which always has the highest values;this may be due to a very high grade of nuclei, typical for comedo DCIS.) Thestandard deviations behave in the same way, increasing from normal to invasivecarcinoma. The increase in standard deviation indicates a larger inter-slidevariation in the group.Table11.THEMEANVALUESOFTHEREPRESENTATIVEFEATURESANDTHEIRVARIANCESINSIXGROUPSOFBREASTDISEASES.GROUPFEATURES,normalnon-proliferativeproliferativenon-comedocomedoinvasive*VARIANCEStissuediseasediseaseDCISDCIScarcinomaIOD1.08(0.13)1.18(0.16)1.14(0.16)1.30(0.35)1.62(0.51)1.50(0.57)*VIOD0.18(0.06)0.25(0.10)0.24(0.07)0.34(0.15)0.55(0.24)0.45(0.27)area708(123)867(65)953(243)1027(332)1635(383)1262(436)•j area141(34)211(83)231(92)273(107)498(192)336(175)varradius9.5(3.6)10.6(3.1)17.5(10.3)18.2(12.6)33.1(15.0)25.8(13.7)•Vvarradius8.2(2.9)9.8(3.0)11.6(3.8)12.8(5.5)22.1(7.6)15.7(6.9)ODvar0.35(0.07)0.39(0.04)0.39(0.09)0.39(0.09)0.46(0.07)0.43(0.09)*JODvar0.060(0.023)0.064(0.016)0.066(0.015)0.069(0.028)0.093(0.037)0.080(0.029)N°OFCASES53818602137Meanvaluesof thefeatureswerecalculatedontheslidebyslidebasis.Themeanvaluesof eachgroupandthestandarddeviationsof themeansarepresented.86Even though the mean values of features increase, a single feature is notsufficient to discriminate between the different groups. Therefore, combinationsof two features were used to display the differences between groups on scatterplots where six points represent the mean feature values for six groups and theerror bars show the standard error of the means. Figure 24 shows the increaseof DNA content and nuclear size from normal tissue to invasive carcinoma, withthe highest values in comedo DCIS. Non-proliferative and proliferative groupare poorly distinguished by these two features. Figure 25 shows the distributionof groups by variation of radius and variance of IOD. It demonstrates how twofeatures can be complementary in distinguishing different groups. The nuclearshape (represented by the variation of radius) of non-proliferative and normalgroup are similar. However, the non-proliferative group has a higher variation ofnuclear DNA content on slides. The opposite situation pertains to non-proliferative and proliferative groups. They have a similar inter-nuclear variationof the DNA content, but can be distinguished by the more irregular nuclearshape of the proliferative group.4.5 HETEROGENEITY OF DUCTAL CARCINOMA IN SITUThe main histological types of DCIS are shown in Figures 26-29.Different histological types of DCIS are often present adjacent to each other inthe same biopsy. The scatter plots of two features are shown for two such casesin Figures 30-31. The first case is an example of carcinoma in situ wheredifferent histological types seem to be a uniform population of cells. The secondgraph presents an example of more usual cases, where different types of DCIS87A: NORMAL GLANDS9: NON—PROLIFERATIVE DISEASEC: PROLIFERATIVE DISEASED: NON—COMEDO DCISE: COMEDO DCISF: INVASIVE CARCINOMA•1 500• TD• IFCW 1000 TIBA5000. 75 1. 00 1. 25 1. 50 1. 75 2. 00IOD (a. u. )Figure 24. NUCLEAR AREA VS. IOD SCATTER PLOTS OF SIX GROUPS OF BREASTDISEASES.There is an increase of DNA content and nuclear size from normal tissue tomalignant tissue. Comedo DCIS has the highest DNA content and nuclear size.88A: NORMAL GLANDSEASED: NON—COMEDO DCISE: COMEDO DCISE: INVASIVE CARCINOMA0. 60.5LF0.4oW Q30zc_i_0.2 -Al0.17. 0 1 2. 6 1 8. 2 23. 8 29. 4 35. 0VARIATION OF RADIUS (a. u. )Figure 25. VARIATION OF RADIUS VS. VARIANCE OF IOD SCATTER PLOTS OF SIXGROUPS OF BREAST DISEASES.Variation of radius increases with more irregular nuclear shape. Variance ofintegrated optical density is based on the internuclear variation of the DNAcontent. The figure demonstrates how two features can be complementary indistinguishing histopathofogical groups. Groups A and B are similar in theshape feature, but group B has the variation of nuclear DNA content on slideshigher than A. Groups B and C have similar inter-nuclear variation of the DNAcontent, but can be distinguished by the more irregular nuclear shape of groupC.89Figure 26. CRIBRIFORM DCIS..•_%h. d‘.,.—‘. ,jp44k,•1.24i%;1L.., ..;‘.-eab’ •“ •% j.‘ ,.p.4. . -‘ i1•_4,j.4.;01•S% .•, c1%a1r— .‘. S4.•Haematoxylin-eosin stain. Magnification on the print is approximately 172:1.90Figure 27. CONFLUENT DCIS.Haematoxylin-eosin stain. Magnification on the print is approximately 172:1.91Figure 28. PAPILLARY DCIS.Haematoxylin-eosin stain. Magnification on the print is approximately 172:1.92Figure 29. COM EDO DCS.b -t( 4-:I‘a- F:‘V:4..4t’—;S_.._fIIHaematoxyHn-eosin stain. Magnification on the print is approximate’y 172:1.93dLaJw—JC-)zIOD(a. u.)Figure 30. AREA VS. IOD SCATTER PLOTS OF VARIOUS DCIS TYPES PRESENT ON THESAME SLIDE: CASE A.Comedo, cribriform and confluent DCIS were present in the same tissue section.DCIS cells of different histological types had similar DNA content and nuclearCCA ciC35002800210014007000A xAAAzaCONFLUENT OCISA CRIBRWORM DCISCOMEDO OCISc NORMAL LOBULESLYMPHOCYTES0 1 2 3 4 5size.Figure 31 AREA VS. IOD SCATTER PLOTS OF VARIOUS DCIS TYPES PRESENT ON THESAME SLIDE: CASE B.Comedo, cribriform and confluent DCIS were present in the same tissue section.DCIS cells of different histological types differ in the DNA content and nuclear944COMEDO DCISc CRIBRI FORMCONFLUENT0 NORMAL LOBULESK LYMPHOCYTESVaw-JC-)DzUEl“U30002400180012006000CEl[]DU U‘IU10 1 2 3IOD(a. u.)4 5size.95seem to be composed of distinct populations of cells with different DNA content,size, and proliferation rate.4.5.A Comparison of comedo and non-comedo typesThe purpose of the study was to characterize the differences betweencomedo and non-comedo DCIS on the basis of nuclear features. Thedifferences in ploidy and DNA histogram parameters between comedo and noncomedo DCIS are presented in Tables 12 and 13. The comedo type has a muchhigher rate of aneuploidy in addition to higher IOD, average DNA index, entropyand exceeding rates.Pictures of non-comedo and comedo nuclei are shown in Figure 32.Parametric and non-parametric T-tests were applied on the slide means andstandard deviations of features to select the features that were significantlydifferent between two groups of slides (p<O.05). Some of the quantitativenuclear features which were significantly different between non-comedo DCISand comedo DCIS slides are listed in Table 14. The data indicate that comedonuclei are larger (1, 2), have more irregular shape (3, 4) and contour (5) andhigher DNA content (6). Comedo nuclei more distinctly manifest dark clumps onthe bright background (11). There is an increased number of high and lowdensity clusters (16,17). Low, medium and high density areas have moreirregular shape (13,14). Gray level variation in the nucleus is larger (7), butsmoother; it occurs at lower spatial frequency (12). The differences betweennuclei on comedo slides are larger than on non-comedo slides. The variation of96Table 12. PLOIDY IN COMEDO AND NON-COMEDO DCIS.TYPE DIPLOID ANEUPLOID NUMBER OF CASESNON-COMEDO 25 (42%) 34 (58%) 59COMEDO 1 (5%) 20 (95%) 21TOTAL 26 (32.5%) 54 (67.5%) 80Aneuploidy was demonstrated in 95% of comedo cases, and in58% of non-comedo cases. Overall, 67.5% of DCIS wereaneuploid.97Table 13. DNA HISTOGRAM PARAMETERS OF DIFFERENT HISTOLOGICAL TYPES OFDCIS. EACH VALUE REPRESENTS THE AVERAGE VALUE OF THE SAMEHISTOLOGICAL TYPES.HISTOLOGICAL NUMBER OF IOD DI %>1 .25 %2.5TYPE CASESCRIBRIFORM 11 1.12 1.05 23 0PAPILLARY 5 1.64 1.7 63 17CONFLUENTIACINAR 24 1.38 1.3 45 1.5MIXED 13 1.30 1.45 35 1NONSPECIFIC 7 1.15 1.15 35 6ALL NON-COMEDO 60 1.30V1.38 40 2ALL COMEDO V 21 1.62 1.53 62 11Table shows the differences in histogram parameters between varioushistological types of DCIS. integrated optical density (IOD), DNA index (Dl), and1.25- and 2.5-exceeding rates are the lowest in cribriform type and the highest incomedo type of DCIS. Of all non-comedo DCIS the papillary type appears to bemost similar to comedo DC IS.aFigure 32. MICROGRAPH OF NON-COMEDO (A) AND COMEDO (B) NUCLEI.98ABThionin-S02stain, Orange-G counterstain. Magnification on the print isapproximately 430:1.0I4c_ ,Ia-0W0bbbowo<><<<<<<<<<<<<<ZZ)’OOXCI)QclDr-,OO>mtJ<c)’mc,ciOxDfl1>)‘<)‘‘;v)‘>oXr-0,i)’gm)‘C)D)’3XG)mmozmC,ZZz-Im!I!Z m C,)Co-n m -I mCDCDCD•<Ø)CDCDc.<5.CQ.CD_.-I.DCDOCD -I.Q)0.-‘U)Cl)CD0 —.Cl)03A PC,)Ocö 1•— 0 0 CD -0Cl)oC) zm ci—ciz0-1 ci T1 ‘1 m m z 0 m Cl) z -n m c ‘ii Cl) 0 -Ti z 0 z (i) 0 m ci 0 z cibbbPPPCObbDCDa)030110301-CO.010100e9!O)—..CD(.90(.9-CO-CO.(.903-.-—.0.01FJCOQ0000I300.a)(.9(.900)03(.9-0CD0101CD40)CD00CD(7’ZOOOO%)CD-OCa0a)0)(,3CDr)•a)01030CD03CDiCO030....0)a)03.1C0..22h)oDlIoo,-zD)O00PPPP03-CD.cC’PPP—--Pi.PP--cjia)P--(.9----o0O.1C.903000-C.900.%4r).1I30O1%)-044(.9CD(7’4P90-.000000000000000000000000000000000000000000000000000000000000003000P90C,3P90000000000000000-0000‘.0 I’D100size (18), shape (19, 20) and DNA content (21) is higher. The inter-nuclearvariation of a number of texture features is also increased in comedo cases.There are larger differences among nuclei in gray level variation (28, 29, 30) andin area (22), density (23), shape (24), and number (26, 27), of low, medium orhigh density clusters.In addition, a stepwise discriminant function analysis was performed onthe cell by cell basis to distinguish comedo and non-comedo nuclei (9541 noncomedo nuclei vs. 2826 comedo nuclei). A combination of 10 features, whichefficiently discriminated two groups of nuclei was selected: AREA, ODKURTOSIS, VAR INT, VAR OD, CRL, NM, CORRELATION, MEAN RADIUS,AVERANGE, and OD SKEWNESS. The classification did not significantlyimprove after forcing more features into discriminant function. The relationshipbetween the number of the employed features and the rate of correctclassification is shown in Figure 33. The discrimination between comedo andnon-comedo nuclei was successful in 83% when 10 features were used. Theresults are shown in the jackknife classification matrix in Table 15.4.5.B Quantitative nuclear features in different histological typesof non-comedo DCISNon-comedo DCIS were grouped according to their histological type(Table 3). Mean values and standard deviations of means were calculated forall nuclear features and their variances. In addition, DNA histogram parametersand ploidy were analyzed.Table 16 shows differences between various histological types of DCIS,10185U)-J-JwC-)180.W750C-)U070 I I0 5 10 15 20 25 30THE NUMBER OF FEATURES USEDFigure 33. COMEDO VERSUS NON-COMEDO NUCLEI: THE RELATIONSHIP BETWEENTHE NUMBER OF THE EMPLOYED FEATURES AND THE RATE OF CORRECTCLASSIFICATION.The discriminant function was performed on the cell by Cell basis. A Combinationof 10 features was selected: AREA, OD KURTOSIS, VAR INT, VAR OD, CRL,NM, CORRELATION, MEAN RADIUS, AVERANGE, and OD SKEWNESS. Theuse of additional features did not significantly improve the discrimination.102Table 15. JACKKNIFED CLASSIFICATION OF NON-COMEDO AND COMEDODCIS NUCLEI.CLASSIFIED GROUPSACTUAL GROUPS NON-COMEDO COMEDO TOTALNON-COMEDO 8185 (86%) 1356(14%) 9541COMEDO 736 (26%) 2090 (74%) 2826Overall classification is correct in 83% (10275/1 2367) of nuclei.Table16.THEMEANVALUESOFTHEREPRESENTATIVEFEATURES,ANDTHEIRVARIANCES,INDIFFERENTHISTOLOGICALTYPESOFDCIS____________HISTOLOGICALTYPEOFDCISFEATURESPAPILLARYCONFLUENTIACINARCRIBRIFORMMIXEDNONSPECIFICTOTALIOD1.64(0.59)1.37(0.30)1.12(0.17)1.29(0.27)1.14(0.51)1.30(0.35)*VIOD0.47(0.26)0.35(0.10)0.25(0.10)0.35(0.19)0.34(0.14)0.34(0.15)area1106(400)1052(297)895(240)925(346)1295(391)1027(332)*Varea285(81)280(106)231(89)249(93)353(149)273(107)varradius22.3(12.9)17.9(12.8)15.7(8.0)13.8(12.8)28.6(14.2)18.2(12.6)ODvar0.40(0.12)0.39(0.06)0.34(0.07)0.35(0.09)0.47(0.12)0.39(0.09)N°OFCASES5241113760MeanfeaturevaluesandstandarddeviationsofthemeansarepresentedinthetableseparatelyforvarioushistologicaltypesofDCIS.Mixedtypereferstocaseswithdifferentcombinationsofconfluent,cribrifom,andpapillarytype.Non-specifictypereferstopatternswhichdidnotfitintoanydiagnosticcategory.*VIODandVareaarefeaturevariances.104demonstrated by the mean values, and their standard deviations, of four featuresand two feature variances. IOD, AREA, VAR RAD and VAR 00 were chosen torepresent the main feature groups (DNA content, area, shape, chromatintexture). VIOD and VAREA were chosen as the two most meaningful variances,which represent the inter-nuclear variation in the DNA content and in nuclearsize on each slide. Table 16 demonstrates that differences between the typesexist in the DNA content, size, shape, and chromatin distribution. The nuclearDNA content, nuclear size, the irregularity of shape and the width of the opticaldensity distribution in the nucleus all increase, with the lowest values incribriform type, intermediate values in mixed and confluent types, and thehighest values in papillary type. The differences in these features between mainhistological types of DCIS are also shown in Figures 34-37. The intraslidevariation of nuclear DNA content and size, shown by the two variances, is alsothe smallest in cribriform, and the highest in papillary type. Figure 38 shows thevariance of lOD in different DCIS types. Inter-slide variation is represented bythe standard deviations of the mean values. For most features, papillary typehas the highest variability between cases, and cribriform group has the lowest.Non-specific type has a low nuclear DNA content, large nuclear size, largeshape irregularity, and wide distribution of optical density values in the nucleus.In addition, nonspecific type has a high inter-slide variations compared to othertypes, which indicates that very different cases are included in this group.Table 13 shows that the DNA histogram parameters also differ betweenthe histological types; the average values are shown for all cases of the samehistological type. A higher 100 of confluent and papillary type corresponds to a105CRIBRIFORM (11)o MIXED (1 3)3000 0 CONFLUENT (24)s PAPILLARY (5)+ NONSPECIFIC (7)A COMEDO(21)AA2000 + A00 A4- A0 -B1000 0o +A0DCIS typeFigure 34. NUCLEAR AREA IN DIFFERENT DCIS TYPES.Cribriform and mixed type have smaller nuclear area than other histologicaltypes of DCIS. Comedo type has the largest nuclear area.106CRIBRIFORM (ii+ NONSPECIFIC (7o MIXED (1 3)O CONFLUENT (24)c> PAPILLARY (5)A COMEDO(21)AA+A2o A—A+1 + A*0DCIS typeFigure 35. IOD IN DIFFERENT DCIS TYPES.Integrated optical density is related to the nuclear DNA content. Cribriform andnonspecific type have a low DNA content. Comedo type has the highest DNAcontent.107o MIXED (1 3)A CRIBRIFORM (110 CONFLUENT (24<) PAPILLARY (5) A+ NONSPECIFIC (7)A COMEDO(21)60(f) +±A4ALj 40 +0 Ao 0 AA C + A20 A + A0+ AA +A8 00DCIS typeFigure 36. VARIATION OF RADIUS IN DIFFERENT DCIS TYPES.Mixed, cribriform and confluent type have more regular nuclear shape than othertypes. Nuclei with the most irregular shape can be found in comedo type.108CRIBRIFORM (ii)0 MIXED (i 3)0 8 CONFLUENT (24)*) PAPILLARY (5)>— + NONSPECIFIC (7)A COMEDO (21)(ii) +z AuJ 0.60 +C) C0DCIS typeFigure 37. VARIATION OF OPTICAL DENSITY IN DIFFERENT DCIS TYPES.Variation of optical density represents the variation in the distribution of opticaldensity values over the pixels of a nucleus. Cribriform and mixed types havemore homogeneously stained chromatin than other types.109CRIBRIFORM (ii+ NONSPECIFIC (7D CONFLUENT (24o MIXED (1 3)> PAPILLARY (5) AA COMEDO(21)A0.8AA0 AALi... + oo ° AA(_) 0z•< 0.4 +A * 8+A> A + A0.2+0. 0DCIS typeFigure 38. VARIANCE OF IOD IN DIFFERENT DCIS TYPES.The variation of the DNA content between nuclei on each slide is the lowest forthe cribriform DC IS. Comedo type has the highest internuclear variation of theDNA content.110Table 17. PLOIDY OF DIFFERENT HISTOLOGICAL TYPES OF NON-COMEDO DCIS.TYPE DIPLOID ANEUPLOID number of casesCRIBRIFORM 7(64%) 4(36%) 11PAPILLARY 2 (40%) 3 (60%) 5CONFLUENT 9 (37.5%) 15 (62.5%) 24MIXED 4 (33%) 8 (67%) 12NONSPECIFIC 3 (43%) 4 (57%) 7Cribriform type appears to have lower frequency of aneuploid casesthan other types. In total, 58% (34/59) of non-comedo DCIS isaneuploid.111higher DNA index (increased DNA content in G01G-phase) and higherexceeding rates (increased number of either proliferating or aneuploid cells).Table 17 presents the ploidy of different histological types of non-comedoDCIS. Cribriform type appears different from the others in the low proportion ofaneuploid cases. Aneuploidy is found almost twice as often in the otherhistological types than in cribriform.4.6. DIFFERENCES BETWEEN PURE DCIS AND DCIS ASSOCIATEDWITH INVASIVE CARCINOMA IN THE SURROUNDING TISSUETable 18 shows the DNA histogram parameters of different histologicaltypes of DCIS and demonstrates a comparison of plain DCIS (DCISI) and DCISassociated with invasive breast carcinoma (DCIS2). Comedo DCIS and some ofthe non-comedo types (papillary, non-specific and mixed) show an increase inthe average DNA index, IOD, and exceeding rates, when invasive carcinoma ispresent in the surrounding tissue. The confluent type shows increased IOD, butsimilar Dl and exceeding rates, while the cribriform type exhibits slightlydecreased values of histogram parameters when it is associated with invasivecarcinoma.Table 19 compares ploidy of DCISI and DCIS2. In non-comedo typeaneuploidy rate increases from 53% in DCISI to 62% in DCIS2. This differenceis not statistically significant. For the comedo type, the proportion of aneuploidcases is decreased in DCIS2, but this is due to a single diploid case found in thisgroup. A further analysis of the differences between DCISI and DCIS2 wascontinued separately for comedo and non-comedo type.Table18.AVERAGEVALUESOFDNAHISTOGRAMPARAMETERSOBTAINEDFROMDIFFERENTHISTOLOGICALTYPESOFDCIS(WITHORWITHOUTASSOCIATEDINVASIVECARCINOMAINTHESUROUNDINGTISSUE).DCISWITHOUTINVASIVECARCINOMADCISWITHINVASIVECARCINOMAIntegratedopticaldensity(IOD),DNAindex(DI),1.25-and2.5-exceedingratesareshownforpureductalcarcinomainsituandductalcarcinomainsitu,whichisassociatedwithinvasivecarcinoma.HISTOLOGICALN°ofIOD(a)DI%>1.25%>2.5N°ofIOD(a)DI%>1.25%>2.5TYPEcasescasescribriform61.17(0.22)1.131051.06(0.08)1.0130papillary21.41(0.45)1.6461231.79(0.71)1.87421confluentlacinar121.33(0.25)1.3441.5121.41(0.34)1.35461.5mixed71.19(0.24)1.327161.41(0.26)1.65451nonspecific40.90(0.12)0.911031.51(0.63)1.456615allnoncomedo311.21(0.27)1.35341291.41(0.42)1.4463.5allcomedo61.45(0.32)1.4573151.69(0.57)1.556414113Table 19. PLOIDY OF PLAIN DCIS (DcIsl) AND DCIS ASSOCIATED WITH INVASIVECARCINOMA IN SURROUNDING BREAST TISSUE (DCIS2).HISTOLOGICAL TYPE DIPLOID ANEUPLOID NUMBER OF CASESNON-COMEDO DCISI 14 (47%) 16 (53%) 30NON-COMEDO DCIS2 11 (38%) 18 (62%) 29COMEDO DCISI 0 6 (100%) 6COMEDO DCIS2 1 (7%) 14(93%) 15Comedo and non-comedo types are presented separately. In total,aneuploidy is found in 61 % of DCISI (22/36) and in 72% of DCIS2 (32/44).1144.6.A Non-comedo typeThe purpose of the statistical analysis was: i) to demonstrate thedifferences in nuclear features between DCISI and DCIS2, and ii) to classifycases to DCISI or DCIS2 on the basis of discriminating nuclear features.Classification was approached in two ways. One approach was to classify caseswith the discriminant function, performed on a slide by slide basis. Thealternative approach was to first classify single nuclei, and then discriminate theslides on the basis of the percentage of the nuclei classified as DCISI or DCIS2.The number of cases and of nuclei in each group is shown in Table 3. In spite ofthe differences between histological types of non-comedo DC IS, various typeswere included, with the condition that they were equally distributed in the DCISIand DCIS2 group. Figure 39 shows examples of non-comedo nuclei collectedfrom DCISI and DCIS2.First parametric and non-parametric tests were applied on the means andstandard deviations of features in order to select the features that weresignificantly different between DCISI and DCIS2 (p<O.05). Some of thefeatures, which were shown to be significantly different by non-parametric testsor T-tests are listed in Table 20 (the p-values of non-parametric test are shown).Table 20 shows that nuclei of DCIS with invasion in the surroundingbreast tissue are rounder, less elongated (1). The fraction of nuclear areaoccupied with high density chromatin has more irregular shape (4). High densitypixels are on average more distant from the center of the nucleus (5). There is alarger number of medium density clusters (6). The variation of gray scaleAB— IFigure 39. EXAMPLES OF DCIS1 (A) AND DCIS2 (B) NUCLEI: NON-COMEDO TYPE.Thionin-S02stain, Orange-C counterstain. Magnification on the print isapproximately 430:1.I-II-Ia,a)Cl0)-tc)C1-C4-C’I-0C4>000000000o.oddddddddUi>U)zIIU)C-)a0aUi01z0zazU)C)az0U)zI—D0II‘-0)CD0)•C4C’1o--0)--FCl)—d0C)‘-U)doc’jI0)0)CDIL)IL)c’,C.)zeeq-co0)‘-—0)(1—‘0_-c)r).COCD—-c)—Cv)Odo’C.)4cóCCozCD0Cv)I.U)U)C-)a00Ui09z0zzUiC’.JU)C)aI-U)a)C,zazD0ci::DU)UiII—zUiC-)zC.)a)0CC.4-4-Ca).Cc1)a)I,.C’-’4-G)C::,Cuca..C0a)-xCOG)09D0.1(00G)C0LLzQiI-U).<ic:-CIiiLLXWZ>’>>-C’1C’)’lU)CDF.CO0)117values is larger, but smoother, it occurs at a lower spatial frequency (2, 3). Insamples of DCIS with associated invasion, there is an increased inter-nuclearvariation of chromatin distribution, demonstrated by significantly larger variancesof some texture features, such as the following variances: Irregularity of the highdensity area shape (7), number of medium density clusters (8), and fractal areal(9).The next step was discriminant function analysis on a slide by slide basiswithout stepwise variable selection. One feature with lowest p-value was forcedto enter the discriminant analysis from each of four feature categories:Photometric features, shape features, continuous texture features and discretetexture features. The number of features was limited, since there was a smallnumber of cases in each group. The features which entered the discriminantanalysis consisted of: i) lOD (photometric feature, p=O.088), ii) ELONGATION(shape feature, p=O.034), iii) FRACTAL AREA I (continuous texture feature,p=O.O1 1), and iv) VNM (discrete texture feature, p=O.OO1). With the stepwisediscriminant function analysis the combinations of 2 and 3 features, that gavethe best discrimination between non-comedo DCISI and non-comedo DCIS2,were selected. With only two features (VNM and ELONGATION) theclassification of cases was correct in 72%. With the addition of the third feature(IOD), the classification improved to 80% correct (Table 21). The jackknifeàlassification matrix is also shown in Table 21. Due to the number of cases inthe analysis it was not appropriate to use more than three features.The second approach to discriminate non-comedo DCISI and DCIS2cases was with the use of the discriminant function formed on a cell by cell118Table 21. PURE DCIS Vs. DCIS WITH ADJACENT INVASIVE CARCINOMA: NONCOMEDO TYPE.A CLASSIFIED GROUPSACTUAL GROUPS DCISI DCIS2 TOTALDCISI 28 (87%) 4 (13%) 32DCIS2 8 (18%) 21 (72%) 29B CLASSIFIED GROUPSACTUAL GROUPS DCISI DCIS2 TOTALDCISI 27 (84%) 5 (16%) 32DCIS2 9 (31%) 20 (69%) 29Classification matrix (A) and jackknifed classification matrix (B) were obtained bythe slide by slide discriminant function analysis. The analysis was performedwith the use of the following three features: IOD, VNM and ELONGATION.These three features were chosen in the stepwise procedure from the set of fourfeatures which were forced to enter the analysis. Overall classification is correctin 80% (49/61) of cases. Classification obtained by the jackknife procedure iscorrect in 77% (47/61) of cases.119basis, followed by the classification of slides with a threshold for the proportionof DCISI or DCIS2 nuclei in each case. Due to the variations in the number ofnuclei per case in both groups, 100 nuclei were randomly selected from eachfeature file to enter the analysis. In that way, every DCIS case could participatewith a similar number of nuclei.The stepwise procedure was applied on two groups of nuclei to separatenon-comedo DCISI (3006 nuclei) and non-comedo DCIS2 (2668 nuclei). Thecombination of features, which was most efficient at distinguishing two groups,was selected. The relationship between number of features used and thepercentage of correctly classified nuclei is shown in Figure 40. The followingfeatures were selected by a stepwise procedure: AREA, VAR INT, TARM,TERL, TERH, ADM, MHAER, C-MASS, C-MASSL, OD MAX, VAR OD,ELONGATION, ENERGY, and CONTRAST. The classification of nuclei with thediscriminant function on a cell by cell basis was correct in 68% of the cases(Table 22).Discriminant function coefficients were then applied on the original (nottruncated) feature files of slides to classify single nuclei. The proportion of cellswith features characteristic of DCIS2 was typically higher on the slides of DCIS2group, than on the slides of the DCISI group. A threshold, which most efficientlydistinguished the slides, was selected for the proportion of nuclei recognized asDCI5I or DCIS2. Slides were than classified on the basis of this threshold. Athreshold of 37% best separated DCISI and DCIS2 cases: Most DCISI caseshad less than 37% of nuclei recognized as DCIS2 nuclei, and majority of DCIS212075U)-J-Jw(-)70UUU)-J° 65>--JF—C-)Uo 60C)055 I0 5 10 15 20 25 30THE NUMBER OF FEATURES USEDFigure 40. NON-COMEDO DCISI VERSUS NON-COMEDO DCIS2 NUCLEI: THERELATIONSHIP BETWEEN THE NUMBER OF THE EMPLOYED FEATURES ANDTHE RATE OF CORRECT CLASSIFICATION.The discriminant function was performed on the cell by cell basis. Acombination of 14 features was selected: AREA, VAR INT, TARM, TERL,TERH, ADM, MHAER, C-MASS, C-MASSL, CD MAX, VAR OD,ELONGATION, ENERGY, and CONTRAST. The use of additional featuresdid not significantly improve the discrimination.121Table 22. JACKKNIFED CLASSIFICATION OF NON-COMEDO DCISI AND NONCOMEDO DCIS2 NUCLEI.CLASSIFIED GROUPSACTUAL GROUPS DCISI DCIS2 TOTALDCISI 2031 (68%) 975 (32%) 3006DCIS2 824 (31%) 1844 (69%) 2668Overall Classification is correct in 68% (3875/5674) of nuclei.122Table 23. DISCRIMINATION OF NON-COMEDO DCISI CASES AND NON-COMEDODCIS2 CASES BASED ON THE PROPORTION OF DCIS NUCLEI ON THESLIDES.CLASSIFIED GROUPSACTUAL GROUPS DCISI DCIS2 TOTALDCISI 21 (68%) 10 (32%) 31DCIS2 4 (14%) 25 (86%) 29The threshold of 37% DCIS2 nuclei was the best in distinguishing DCISIand DCIS2 slides. DCIS2 slides contained more than 37% of DCIS2nuclei and DCISI slides contained less than 37% of DCIS2 nuclei.Overall classification is correct in 77% (46/60) of cases.123cases had more than 37% nuclei classified as DCIS2 nuclei (Figure 41). Withthis threshold 77% of cases (slides) were correctly classified (Table 23).4.6.B Comedo typeComedo DCIS without associated invasive carcinoma (DCISI) andcomedo DCIS with associated invasive carcinoma in the surrounding breast(DCIS2) were analyzed. The purpose of statistical analysis was: i) todemonstrate the differences in nuclear features between comedo DCISI andDCIS2, and ii) to obtain a classification system on the basis of quantitativefeatures. Examples of comedo DCISI and DCIS2 nuclei are shown in Figure 42.Feature means and variances were tested by T-tests and non-parametrictests to find the differences between two groups. The differences were acceptedas significant if the p value of the test was less than 0.05. Some of the featureswith significant differences between the two groups as shown by their p-value ofnon-parametric tests (p<0.05) are listed in Table 24.The features listed in the Table 24 indicate that the maximum densityspots are darker (1) in comedo nuclei when there is an associated invasivecarcinoma in the surrounding breast tissue (DCIS2 nuclei). The samples ofcomedo DCIS2 have increased inter-nuclear variation of the DNA content (2).DCIS2 samples also have a larger inter-nuclear variation of various texturefeatures, for example relative nuclear area occupied by medium densitychromatin (3), relative density of medium density chromatin (4), and maximaloptical density (5). The number of cases (6 vs. 15) that were analyzed was toosmall to run the discriminant function analysis on the slide by slide basis.124DCIS2 slidesA AAAAA AA AA MA__ vwv V VW V V V VVVWV VDCIS1 slides0 20 40 60 80 100the percentage of DCIS2 nuclei on individual slides (%)Figure 41. CLASSIFICATION OF DCISI (A) AND DCIS2 (B) CASES: NONCOMEDO TYPE.A classification of cases (patients) was based on the proportion of DCIS2 nucleion individual slides. A threshold of 37% was chosen because it separatedcorrectly the highest number of cases.•IAI —Figure 42. EXAMPLES OF DCISI (A) AND DCIS2 (B) NUCLEi: COMEDO TYPE.Thionin-S02 stain, Orange-G counterstain. Magnification on the print isapproximately 172:1.A 1251I’IFI•6.4iI’ABr1a)ccoC1-C100q0000000.0000000IzLU‘.-—IOC)’0CD-C10C100-dod—adC)•—10I.’øc41co--eC)Eddaddad-%‘-‘-‘-0C1C)‘b00000-ddoo--ooC.)Cco,.(C100)000)U)C’100C)C10Edodddod0LU00zLUC’.’U)0I—CI)a:U)0z0zD0a:a:DC/)LiiII—z0z0a:C)LU>C/)zU)C 4 -.CU).2Eca“-CCU)0c:0i.CI)U)0U)-CU)0.CU4-CO.a,Q0.i-i.0.Xww><D<wccxSi--OOoi:iq,I,>>>>C’)IOCDI127However, the analysis on the a cell by cell basis was performed to distinguishDCISI (693 nuclei) and DCIS2 (2133 nuclei). The relationship between thenumber of the employed features and the rate of correct classification is shownin Figure 43. The combination of features selected by the stepwise procedureincluded: VAR RAD, TARL, NL, OD MAX, VAR 00, and FRACTAL AREA 1.With the discriminant function on a cell by cell basis a correct classification ofDCISI and DCIS2 nuclei was achieved in 82% (Table 25).The classification of the slides with a threshold was performed in thesame manner, as was described in the previous section for the analysis of noncomedo cases. The threshold of 50% was chosen for the proportion of DCISIand DCIS2 nuclei on the slides (Figure 44). With this threshold, all DCISI andDCIS2 cases of comedo type were correctly classified (Table 26).4.7 MALIGNANCY ASSOCIATED CHANGES (MAC)Table 4 shows the number of cases and nuclei involved in this analysis.The aim of the statistical analysis was to distinguish benign breast tissues andtissues of patients with breast malignancy on the basis of measurements ofnuclei from normal appearing lobules. Examples of normal nuclei from benigncases and normal nuclei from invasive carcinoma cases are shown in Figure 45.First, the discriminant function analysis was applied on normal nuclei frombenign cases (normal) and on normal nuclei from invasive carcinoma cases(“MAC”). The combination of features selected by the stepwise procedureconsisted of following features: TARL, TERM, ADH, MAER, 00 MAX, ODSKEWNESS, HOMOGENEITY, and FRACTAL AREA 2. The relationshipFigure 43. COMEDO DCISI VERSUS COMEDO DCIS2 NUCLEI: THE RELATIONSHIPBETWEEN THE NUMBER OF THE EMPLOYED FEATURES AND THE RATE OFCORRECT CLASSIFICATION.The discriminant function was performed on the cell by cell basis. Thecombination of 6 features was selected: VAR RAD, TARL, NL, CD MAX, VARCD, and FRACTAL AREA 1. The use of additional features did not significantlyimprove the discrimination.128U-)-J-JC)IDwLiIf)I’)-J(-)>--J(-)bJ0C)U0go8580757065600 5 10 15 20 25THE NUMBER OF FEATURES USED129DCIS2 slidesA A AAAAAAV VW VVDCIS1 &ides0 20 40 60 80 100the percentage of DCIS2 nuclei on individual slides (%)Figure 44. CLASSFICATI0N OF DCISI (A) AND DCIS2 (B) CASES: COMEDO TYPE.A classification of cases (patients) was based on the proportion of DCIS2 nucleion individual slides. A threshold of 50% was chosen because it separatedcorrectly the highest number of cases.130Table 25. JACKKNIFED CLASSIFICATION OF COMEDO DCISI AND COMEDO DCIS2NUCLEI.CLASSIFIED GROUPSACTUAL GROUPS DCISI DCIS2 TOTALDCISI 551 (80%) 142 (20%) 693DCIS2 363 (17%) 1770 (83%) 2133Overall classification is correct in 82% (232 1/2826) of nuclei.131Table 26. DISCRIMINATION OF COMEDO DCISI CASES AND COMEDO DCIS2CASES WITH THE THRESHOLD FOR THE PROPORTION OF DCIS2 NUCLEION SLIDES.CLASSIFIED GROUPSACTUAL GROUPS DCISI DCIS2 TOTALDCISI 6(100%) 0 6DCIS2 0 21 (100%) 21The threshold for the proportion of DCIS2 nuclei on the slides was50%. Overall classification is correct in 100% (36) of cases.1325,‘SSThionin-S02 stain, Orange-G counterstain. Magnification on the print isapproximately 172:1.AB0Figure 45. EXAMPLES OF NORMAL NUCLEI FROM BENIGN CASES (A) AND NORMALNUCLEI FROM INVASIVE CARCINOMA CASES (B).133between the number of the features used in the discriminant function and therate of correct classification is shown in Figure 46. The classification of nucleiwith the discriminant function was correct in 76% of cases (Table 27).The above discriminant function was applied on the nuclei of individualcases to recognize the proportion of “MAC” nuclei on each slide. Mostcarcinoma cases had higher frequency of “MAC” nuclei than benign cases. Thethreshold of 34% “MAC” nuclei was selected. Cases with more than 34% of“MAC” nuclei were classified as carcinoma cases and cases with less than 34%of “MAC” nuclei were classified as benign. With this threshold 91% ofcarcinoma cases and 80% of benign cases were correctly classified (Table 28).Overall, the classification of slides was correct in 86% (37/43). This discriminantfunction was tested on normal nuclei collected from the cases with carcinoma insitu. The proportion of “MAC” nuclei was higher than 34% in seven of the elevencases.The second aim was to distinguish normal nuclei from the same 20 benigncases and normal nuclei from 34 malignant cases. Eleven DCIS cases (577normal nuclei) were added to previously described invasive carcinoma cases.Following features were selected by the stepwise procedure: AREA, TARL,TARH, MAER, 00 MAX, VAR 00, and OD SKEWNESS. The results of thisapproach are shown in Table 29. As described before, the discriminant functionwas applied on the nuclei of individual cases, and the threshold was selected,which most efficiently separated benign and malignant cases (Figure 47). With34% of “MAC” nuclei as a threshold, 82% malignant and 80% of benign caseswere correctly recognized (Table 30). Overall, the classification obtained withthe discriminant function was accurate in 81 % of cases (44/54).13470__J /C) />- /65 /(MAC NUCLEI ORIGINATE FROM PAflENTS60 WITH INVASIVE CARCINOMA)U055 I0 4 8 12 16 20 24THE NUMBER OF FEATURES USEDFigure 46. NORMAL VERSUS MAC” NUCLEI: THE RELATIONSHIP 8E1WEEN THENUMBER OF THE EMPLOYED FEATURES AND THE RATE OF CORRECTCLASSIFICATION.The discriminant function was performed on the cell by cell basis. Thecombination of 8 features was selected: TARL, TERM, ADH, MAER, CD MAX,CD SKEWNESS, HOMOGENEITY, and FRACTAL AREA 2. The use ofadditional features did not significantly improve the discrimination.135Table 27. JACKKNIFED CLASSIFICATION OF NUCLEI TO NORMAL NUCLEI AND“MAC” NUCLEI (A).CLASSIFIED GROUPSACTUAL GROUPS NORMAL MAC TOTALNORMAL 1532 (79%) 399 (13%) 1931MAC 338 (18%) 850 (72%) 1188Images of the nuclei were collected from normal appearing lobules ofbenign cases (the first group) and from normal appearing lobules ofinvasive carcinoma cases (the second group, “MAC”). Overallclassification based on nuclear features is correct in 76% (2382/3119)of nuclei.136Table 28. CLASSIFICATION OF BENIGN CASES AND INVASIVE CARCINOMA CASESACCORDING TO THE PROPORTION OF “MAC” NUCLEI.CLASSIFIED GROUPSACTUAL GROUPS BENIGN MALIGNANT TOTALBENIGN 16(80%) 4(20%) 20MALIGNANT 2(9%) 21 (91%) 23Threshold of 34% MAC nuclei on the slide is used to distinguish benignand malignant cases. Majority of malignant cases have more than onethird of nuclei characteristic of MAC. Majority of benign cases have lessthan of nuclei typical of MAC. Classification is correct in 86% (37/43) ofcases.137Table 29. JACKKNIFED CLASSIFICATION OF NUCLEI TO NORMAL NUCLEI AND“MAC” POSITIVE NUCLEI (B).CLASSIFIED GROUPSACTUAL GROUPS NORMAL MAC TOTALNORMAL 1538 (80%) 393 (20%) 1931MAC 551 (31%) 1214 (69%) 1765Images of the nuclei were collected from normal appearing lobules ofbenign cases (one group) and from normal appearing lobules ofmalignant cases (second group). In the second group DCIS casesare included in addition to invasive carcinoma cases. Overallclassification based on nuclear features is correct in 74% (2752/3696)of nuclei.138slides of patientswith INVASIVE CARCINOMAslides of patientswith DCISo coo co o slides of patients withwith BENIGN BREAST DISEASEI I I I0 20 40 60 80 100the percentage of “MAC” nuclei on individual slides (%)Figure 47. CLASSIFICATION OF MALIGNANT AND BENIGN CASES.The classification was based on the proportion of MAC nuclei on individualslides.139Table 30. CLASSIFICATION OF BENIGN AND MALIGNANT (IN SITU AND INVASIVECARCINOMA) CASES ACCORDING TO THE PROPORTION OF “MAC”NUCLEI.CLASSIFIED GROUPSACTUAL GROUPS BENIGN MALIGNANT TOTALBENIGN 16 (80%) 4(20%) 20DCIS 4(36%) 7 (63%) 11INVASIVE CANCER 2 (9%) 21 (91%) 23Carcinoma in situ is included in this analysis. Threshold of 34% is used todistinguish benign and malignant cases. Majority of malignant cases havemore than one third of nuclei characteristic of MAC. Majority of benigncases have less than of nuclei typical of MAC. The proportion of “MAC”nuclei is higher in invasive carcinoma than in carcinoma in situ.Classification is correct in 81 % (44/54) of cases.1405. DISCUSSIONThe following studies were performed in this thesis: i) DNAmeasurements on tissue sections were evaluated in comparison with othercytometry techniques, ii) the relationship between quantitative nuclear featuresand histopathological classification was examined, iii) different histological DCIStypes were characterized on the basis of nuclear features, iv) differences innuclear morphology between pure ductal carcinoma in situ (DCISI) and ductalcarcinoma in situ with synchronous invasive carcinoma (DCIS2) were defined,and v) changes of nuclear morphology of epithelial cells from normal appearingtissue adjacent to invasive carcinoma (MAC) were analyzed.5.1 COMPARISON OF DIFFERENT CYTOMETRY TECHNIQUESTissue sections are important for morphometric studies such as theanalysis of architectural pattern of tissues, as well as nuclear morphology andchromatin texture describing the DNA distribution in the nuclei. Tissue sectionsare generally not recommended to be used for the DNA ploidy measurementsdue to possible errors based on the sectioned and overlapped nuclei. However,image cytometry (IC) on tissue sections has an advantage of preservedarchitecture and selective sampling of nuclei. This is particularly important forthe analysis of specimens with small areas of diseased tissue or with a variety ofhistological patterns present in the same region, such as premalignant changesor in situ tumors. With IC measurements on breast tissue sections it is possibleto analyze a single small duct filled with malignant cells, which is very useful in141the analysis of small, early tumors. The preserved architecture in sections alsoallows precise selection of the nuclei of interest where a variety of tissuepatterns are present in the same region. This is very important for the analysisof small malignant or premalignant breast lesions which can not be performed byFC. These were the reasons for choosing the measurements on sections as themost appropriate for this study.One of the aims in this thesis was to evaluate the reliability of tissuesection measurements in detecting an aneuploid cell population. For thatpurpose we compared the DNA content measurements of IC performed on tissuesections to the DNA content obtained with other cytometry techniques. First ICof tissue sections and FC were compared on 51 tissue blocks with invasivecarcinoma. Secondly, IC measurements performed on tissue sections werecompared to measurements of corresponding smears and cytospins in sevencases of invasive breast carcinoma.Archival breast specimens were used in the study. There are severaldisadvantages of DNA ploidy analysis of deparaffinized tissues. It has beendemonstrated that formalin fixation alters the staining pattern of nuclei forintercalating dyes or for dyes using the Feulgen procedure (Becker 1990). Thesame study showed the influence of the various chromatin states on thefluorescence intensity obtained by the Propidium iodide staining of formaldehydefixed cells. Such variations of staining may be the cause of bimodal orpseudoaneuploid peaks. It has also been found that the disaggregationprocedure produces a high staining variability (Schimmelpenning 1990). Theembedded tissue measurements show larger coefficients of variation of thepeaks than the measurements performed on fresh tissue. Another consequence142of the staining variability is that external standards can not be applied, so that inFC and in IC measurements, the peak of internal diploid cells (tumor, stromal,inflammatory, normal parenchymal) should be used as the diploid referencevalue. IC measurements on tissue sections have a unique advantage ofpreserved architecture which allows more specific diagnosis of different celltypes and the use of visualized internal control cells.IC of tissue sections in this study showed a relatively narrow DNAdistribution in the diploid cases. There were no problems with the interpretationof diploid histograms. However, in some of the aneuploid cases the IODdistribution obtained with IC of tissue sections was indeed very wide, and thepeak was difficult to select. In part, this was caused by increased irregularity ofshape and size in aneuploid nuclei. As a consequence a higher proportion ofincomplete or overlapped nuclei was included in the measurements. Also, thewide distribution in some IC histograms characteristically accompaniedaneuploid peaks and was probably a reflection of tumor heterogeneity.5.1 .A Flow cytometry and image cytometry of tissue sectionsIC agreed with FC in ploidy in nearly 80% of cases. IC resultscorresponded strongly to FC aneuploid cases (nearly 90%). However, almostone half of FC diploid cases were found to be aneuploid by IC.FC alone detected aneuploid DNA content in five tumors. Three of thesecases had a diploid IC histogram and a FC histogram with Dl values just slightlyabove the artificially set limit of 1.1. They were therefore classified as aneuploidby FC. Near-diploid cases of histograms derived from paraffin embedded tissue143have been questioned previously (Joensuu 1990, FernO 1992, Fossà 1992).The other possibility is that IC has failed to detect these peridiploid peaks. Inthe remaining cases with the discordant ploidy outcome, clear differences in Dlwere evident. IC evidently failed to detect aneuploidy in two and FC in sixcases. These results suggested that no method was superior in detecting theaneuploidy. However, IC seemed to be more efficient if FC peridiploid caseswere not considered. Because all the cells were representative in the IChistograms this method possibly had a better chance to detect an abnormality.Dl of cases where both methods agreed on ploidy showed a poorcorrelation. The disagreements could be explained by the heterogeneity oftumors and differences in sampling, cut and overlapped nuclei in IC on tissuesections, differential staining of disaggregated nuclei for FC, and subjectiveinterpretation of the histograms.The cases with additional DCIS are examples of complex situations whichare often present in breast cancer specimens. It is known that an extensiveDCIS in the specimen with invasive ductal cancer is associated with a high riskof residual intraductal tumor in the remaining breast tissue. Moreover, extensiveDCIS predicts the recurrence of infiltrating ductal cancer after breast-conservingtherapy (Holland 1990). This suggests that the ploidy of DCIS in invasive breastcancer specimens might have a prognostic value. Unfortunately in DNAcytometric studies these situations usually do not receive much attention and theprognostic implications of DNA content of DCIS in invasive cancer specimensare not yet established. Measurements on tissue sections could be useful forsuch studies.144Overall, the results of this study show that, contrary to the present belief,IC performance on tissue sections is not inferior to FC in DNA ploidymeasurements on archival breast cancer tissue. IC can not determine the Dlvalue as accurately as FC, but on the other hand it appears to be more capableof detecting aneuploidy. Also, with the measurements on tissue sections it ispossible to analyze affected tissue confined to a very small area and to selectspecific nuclei of interest when a variety of disease patterns are present in thesame region. In addition, IC can provide more extensive information on nuclearmorphology and structure. Nuclear grade seems to be one of the most reliablepredictors of the outcome in node-negative breast cancer. With image cytometrymeasurements it is possible to determine nuclear grade in an objective way withthe use of a variety of nuclear features which reflect the distribution of thechromatin and the nuclear shape and size as well as the DNA amount(Larsimont 1989). Therefore, IC of tissue sections appears to be a suitablecomplementary method to FC in studying the prognosis of breast cancer.5.1 .B Comparison of different image cytometry techniquesIn seven cases of invasive breast carcinoma three IC techniques wereused to measure the nuclear DNA content. Archival smears of fine needleaspirate and corresponding tissue blocks from subsequent biopsies wereavailable for each patient. Tissue sections and cytospins with disaggregatednuclei were prepared from tissue blocks. IC measurements were then performedin an automated way on smears and cytospins, and manually on tissue sections.145All three techniques detected an aneuploid peak in 3 cases. The cytospinmeasurement failed to show an aneuploid peak in one case. The smear alsofailed in another case. The measurements on tissue sections disagreed with theother two methods in one case, where an additional peak was detected.Reasons for the failure to detect an aneuploid peak could be in tumorheterogeneity and differences in sampling. IC of tissue sections actuallyperformed the best in the detection of aneuploidy as it did not miss any of theaneuploid cases. Moreover, in the case where tissue section measurementdisagreed with the other two techniques by detecting an aneuploid peak, thispeak might have been the result of the selective sampling of a small populationof aneuploid cells which were diluted by other cells in the smear and cytospin.The DNA histogram parameters were determined for all 21 histograms.Agreement between three techniques on the position of the aneuploid peaksoccurred only in two cases. The discrepancies in the DNA index in other casesmay be the consequence of tumor heterogeneity. Other possible reasons for thediscrepancies are: i) the damage to nuclei caused by the disaggregationprocedure for cytospin, ii) a differential staining due to the different fixation ofsmears, or iii) a wide distribution of values in the histograms of tissue sectionsdue to cut and overlapped nuclei. The exceeding rates and entropies of thehistograms differed completely for different techniques because of the differentsampling procedures involved. Generally, values were the highest in histogramsof tissue sections where only carcinoma cells were analyzed.It is clear that the measurements on tissue sections have manydisadvantages, such as the relatively small number of cells that can be analyzedin an acceptable time period (2-3 hours per sample for 200 diagnostic cells and14650 control cells), wide distribution of values in the DNA histograms, and lowaccuracy in the position of the peaks. However, the results of the present studyshowed that the IC measurements of tissue sections are at least as valuable inthe detection of aneuploidy as the automated measurements performed onsmears or cytospins.5.2 THE CORRELATION BETWEEN QUANTITATIVE NUCLEARFEATURES AND ADVANCING MORPHOLOGICAL CHANGESIn the search for new important prognostic characteristics it is important todevelop an objective and reproducible analysis of breast premalignant changesand carcinoma in situ. One approach to developing an objective classification isthe use of IC measurements of nuclear features. IC features expressmorphologic nuclear characteristics in a quantitative way.The use of various nuclear features for diagnostic or prognostic purposesmay offer superior results compared to the DNA content alone. The value ofusing multiple nuclear features (ploidy, texture and morphometric features) in thegrading of invasive breast carcinoma has been recognized in many studies(Stenkvist 1978, Baak 1985, Umbricht 1989, Larsimont 1989, Dawson 1991,Komitowski 1990, Theissig 1991). Quantitative nuclear features reflect themorphological changes which are visualized by the pathologist for the purposeof grading the nuclear atypia, but have the advantage of being more sensitive,more objective and more reproducible.It is thought that in the process of malignant transformation the breasttissue undergoes a sequence of multiple genetic alterations. It has been147postulated that these molecular events are associated with morphologicalalterations. One of the aims of this study was to investigate the relationshipbetween quantitative nuclear features and morphological changes of breasttissue which are used by pathologists for diagnostic purposes.Various breast diseases were analyzed by IC measurements beingperformed on tissue sections. The analyzed cases were then grouped to sixmajor groups: Normal tissue, non-proliferative disease, proliferative disease,non-comedo DCIS, comedo DCIS and invasive carcinoma. Differences inquantitative nuclear features relating to size, shape, nuclear DNA content andtexture were demonstrated between major groups of breast diseases. Thedifferences indicated that it may be possible to use quantitative features fordiagnostic purposes as an adjunct to the visual analysis of tissue morphology.However, this must be further studied on narrower categories of breast diseases.Also, for the purpose of the classification of breast diseases on the basis ofnuclear features the inter-observer disagreements in diagnosis should be takeninto account.The present study showed that changes in nuclear features correlatedwith the increasing histopathological abnormality. These results were inagreement with other studies where a similar increase in the irregularity ofnuclear features was found on tissues other than breast (Bibbo 1989, Petein1991, Mulder 1992, Salmon 1992). Similar observations were made in amorphometric study of Pienta et al. (Pienta 1991). In their study, size and someshape parameters were measured on normal tissue, carcinoma in situ, lymphnode negative, lymph node positive, and metastatic breast cancer. Nuclear area148and the intraslide variation of mean nuclear area increased with more severehistologic changes.In the present study, non-proliferative disease, proliferative disease,DCIS, and invasive carcinoma showed increasing degrees of nuclear alterationscompared to the normal tissue. Increasing abnormalities in the nuclear DNAcontent and chromatin distribution as well as in nuclear size and shape weredemonstrated. The occasional findings of aneuploidy in lesions, such as simplehyperplasia, indicated substantial genetic changes and illustrated thepremalignant nature of these diseases. Aneuploidy of non malignant tissuesuggested higher progressive potential compared to similar tissues with diploidDNA content.In addition to the increase in the mean values of the features, theintraslide and interslide variability showed a similar increase. The lowestinterslide and intraslide variations of shape, size , texture, and DNA content,were found in the normal group. The variations became larger in non-proliferative and proliferative disease, showed further increase in non-comedoDCIS, and were the highest in the invasive carcinoma and comedo DCIS groups.The deviation of quantitative nuclear features increased in parallel withthe severity of the diagnosis and could be indicative of progression. Theprogressive alteration of nuclear features indicates, that nuclear characteristicsof breast diseases, represented by a combination of quantitative, objectivelyanalyzed features, could be important for the prognosis of benign breastdisease, as well as DCIS.1495.3 HETEROGENEITY OF DUCTAL CARCINOMA IN SITUIt is known that DCIS is a heterogeneous group of tumors, with varyingmorphology and with different clinical presentation. Moreover, the progressivepotential and natural history of DCIS cover a wide range of possibilities.The association between aneuploidy of carcinoma in situ and itsprogression to invasive carcinoma was previously demonstrated on cervicaltissue (Bibbo 1989). It has been suggested that aneuploidy may be associatedwith more aggressive nature of carcinoma in situ in the breast (DCIS). In thepresent study the ploidy pattern and DNA histogram parameters of various typesof DCIS were analyzed. DNA histogram parameters included IOD, DNA index,1.25 exceeding rate and 2.5 exceeding rate.Aneuploidy was found in 67% of all DCIS cases. This was in agreementwith previous studies (Crissman 1990, Schimmelpenning 1992, Fisher 1992,Pallis 1992). Differences were characterized in the incidence of aneuploidybetween comedo and non-comedo types and also between different noncomedo types. The high values of histogram parameters and a high proportionof aneuploid cases in certain histological types could be linked to a moreaggressive nature of these tumors.An example is comedo DCIS. The aggressive nature of this DCIS type iswell known. Comedo DCIS is a high grade lesion, which is more oftenassociated with microinvasion, and has a high capacity to recur or to progress toinvasive cancer (Lagios 1989, Patchefsky 1989, Schwartz 1992). On thecontrary, the cribriform type is regarded as an entity with a relatively lowprogressive potential, which is often estrogen receptor positive, and has a low150proliferation rate, low incidence of microinvasion, low frequency of aneuploidy,and infrequent overexpression of c-erbB-2 oncogene (Meyer 1986, Patchefsky1989, Locker 1990, Crissman 1990, Schimmelpenning 1992, Bur 1992).In the present study comedo type showed the highest aneuploidy rate(95%), and the highest values of the DNA histogram parameters of all DCIS.Cribriform type had the lowest values of DNA histogram parameters, and thelowest proportion of aneuploid cases (36%). Other histological types werebetween cribriform and comedo type, with the aneuploidy frequencies of 60-67%. Also, confluent, mixed, and papillary types had intermediate values ofhistogram parameters. These results indicated that aneuploidy and high valuesof DNA histogram parameters were associated with certain types of DCIS whichare believed to have higher progressive potential. Moreover, they suggested theimportance of aneuploidy in the prognosis of individual DCIS cases.One of the aims of the present study was to characterize the diversity ofDCIS histological types on the basis of their quantitative nuclear features.Histological types of non-comedo DCIS are usually identified on the basis oftheir pattern of growth and differences in the architecture rather than on thebasis of their nuclear morphology. In the present study differences in nuclearfeatures between major histological types of DCIS were demonstrated. Inaddition to the nuclear DNA content, other nuclear characteristics, such asshape, size and chromatin texture, showed the same tendency when differenthistological types were compared. The cribriform type had feature values mostsimilar to the features of normal tissue. Feature values showed an increase inthe confluent and mixed types and were even higher in the papillary type ofDCIS. Comedo DCIS had the largest nuclei, most irregular nuclear shape and151the distribution of chromatin which was least similar to the normal cells. Theintraslide variation of nuclear size and of nuclear DNA content (represented byvariances of area and IOD) increased in the same order.These results were in agreement with the increase in aneuploidy rate andin DNA histogram parameters, as described. The repeated increase in theirregularity of nuclear size, shape, and chromatin texture was observed in thesame order of histological types: i) cribriform, ii) mixed and confluent, iii)papillary, and iv) comedo. The results paralleled previously describeddifferences in progressive potential related to the specific histological types ofDCIS. The increased nuclear DNA content and the irregularity of nuclearmorphology were both associated with more aggressive histological types ofDC IS. Furthermore, these results indicated that quantitative analysis of nuclearsize, shape and chromatin texture could have a prognostic value in individualDCIS cases.5.4 DIFFERENCES BETWEEN PURE CARCINOMA IN SITU ANDCARCINOMA IN SITU ASSOCIATED WITH INVASIVE BREASTCARCINOMA IN THE SURROUNDING TISSUEThe differences in ploidy of DCIS without associated invasive cancer(DCIS1) and DCIS with invasive cancer in the surrounding breast (DCIS2) havebeen studied previously. Two different groups reported discordant results(Carpenter 1987B, Fisher 1992). This is an important subject related to theinvasive potential of DC IS. If it were true that there is a higher proportion ofaneuploid cases of DCIS associated with invasive breast carcinoma in the152adjacent tissue, this would favor the association of aneuploidy with a higherprogressive potential of DC IS. Our results showed an overall increase inaneuploidy from 53% in DCISI to 62% in DCIS2 for the non-comedo type. Allcomedo cases except one were aneuploid regardless of the presence orabsence of invasive carcinoma in the adjacent tissue.The morphological differences between DCISI and DCIS2 nuclei havenot been yet described in the literature. In the present study their existence wasconfirmed for the first time. We confirmed that nuclei of DCISI and nuclei ofDCIS2 have differences in morphology, mostly in the chromatin distribution,which can be detected by quantitative analysis of nuclear features.Furthermore, we showed that DCISI and DCIS2 cases could be discriminatedwith a classification system based on quantitative nuclear features. This wasshown both for comedo and non-comedo type of DC IS.Non-comedo tvDeFor non-comedo DCIS, the characteristic changes associated with thepresence of invasive cancer in the neighboring tissue were: I) higher degree ofroundness, and ii) changes in the chromatin texture. Another characteristic ofnon-comedo DCIS2 nuclei was an increased inter-nuclear variation of sometexture features, which corresponds to the increased variability in the chromatindistribution in the sample. With the discriminant function analysis on slide byslide basis we obtained a classification system for non-comedo DCIS basedsolely on the nuclear features. The non-comedo DCIS cases with or withoutinvasive cancer were correctly recognized in 80% of cases. Another approachto discriminate cases with and without associated invasive cancer was usedwhere the discriminant function was formed on a cell by cell basis. The153discriminant function was then applied to the nuclei of each slide to determinethe proportion of DCISI and DCIS2 nuclei on the individual slides. Finally theslides could be distinguished by setting a threshold for the proportion of nucleion each slide which were recognized by the discriminant function as DCISI orDCIS2. The best separating threshold was 37%: Most DCISI slides containedless than 37% DCIS2 nuclei and most DCIS2 slides had more than 37% DCIS2nuclei. The classification of cases with the use of this approach was correct in77% of the sample cases. This represented a good agreement with the slide byslide based classification above.Comedo tvDeThe comparison of comedo DCISI and DCIS2 cases also showeddifferences in nuclear features. DCIS2 cases had higher maximal optical densityvalues, increased variation of various texture features and increased variation inthe nuclear DNA content. It is interesting that the increased variation of nuclearcontent was shown to be an important indicator of bad prognosis in invasivebreast carcinoma (Stenkvist 1982). The discriminant function analysis wasperformed on a cell by cell basis and then comedo cases were classified on thebasis of the frequency of DCISI or DCIS2 nuclei which were present onindividual slides. The classification of comedo cases was successful in all cases(100%).These results are not unexpected. The aggressive nature of comedoDCIS is well known. The aggressive nature of comedo DCIS is well known.This type of DCIS is most often aneuploid (Locker 1990, Schimmelpenning 1992,Killeen 1991, Pallis 1992). Compared to non-comedo DCIS, comedo type muchmore often exhibits an increased expression of c-erbB-2 oncogene, which has154been associated with a bad prognosis in invasive breast carcinoma (van deVijver 1988, Bartkova 1990). Immunostaining of estrogen receptors is moreoften negative in comedo type than in other, better differentiated DCIS types(Bur 1992). The expression of mutant p53 protein, which has been associatedwith genetic instability and tumor progression, is also much more often presentin comedo type than in non-comedo types (Poller 1993). Comedo tumors have ahigher growth rate than other types of DCIS (Meyer 1986). The expression ofnm23, a metastasis suppressor gene, is commonly negative in a proportion ofcells in comedo DCIS while all cells show positive staining in other histologicaltypes of DCIS (Royds 1993). Microinvasion is very commonly found inassociation with comedo type DCIS (Patchefsky 1989) and is often multifocal.This is less frequently seen in the non-comedo types. It has been previouslydemonstrated that comedo DCIS has a higher capacity to recur or to progress toinvasive cancer than other types of DCIS (Lagios 1989, Schwartz 1992).Altogether, comedo DCIS seem to be a uniform group of fast growing highlyprogressive tumors.It is possible to speculate that non-comedo DCIS found in invasivespecimens is more heterogeneous in its progressive potential than comedoDCIS. Some foci of non-comedo DCIS may have the tendency to progress toinvasive carcinoma, while others may still be at much lower stages in theprocess of progression to invasive carcinoma. In such non-comedo cases thenuclear features related to the invasive potential may not be expressed uniformlythroughout the tumor being present only in a proportion of the DCIS ducts.In conclusion, the reason for the morphological differences betweenDCISI and DCIS2 nuclei is not clear. Specific changes in nuclear morphology155characteristic of DCIS associated with invasive carcinoma in the surroundingbreast indicate that quantitative nuclear features may be predictive of thesubsequent behavior of DCIS tumors. These findings are of interest in view oftheir clinical relevance and it is very important to confirm them in further studieson larger groups of patients.5.5 MALIGNANCY ASSOCIATED CHANGESOne of the most important aims of this thesis was to demonstrate theexistence of slight morphological changes in normal appearing breast tissueadjacent to breast carcinoma. These changes were previously described inother tissues as malignancy associated changes (MAC).Only nuclei from normal appearing lobules were analyzed for two groupsof cases. In one group the cases originated from malignant biopsies. In thesecond group the cases originated from benign biopsies with minimalproliferative changes. Normal nuclei from the two groups were then separatedon the basis of their feature values to “true normal’ nuclei and to “MAC” nuclei.The majority of the discriminating features selected by the stepwise procedure inthe discriminant function analysis were texture features, such as nuclear areaoccupied with low density chromatin, the distance of high density chromatin fromthe center of the nucleus, maximum of nuclear optical density, fractal textures,homogeneity of chromatin distribution, and skewness of the optical densitydistribution.With the classification system based on the proportion of “MAC” nuclei oneach slide it was possible to discriminate between malignant and benign cases.156The proportion of “MAC” nuclei was usually more than a third of all nuclei on theslides originating from malignant biopsies. On the contrary, “MAC” nuclei rarelyrepresented more than one third of all nuclei on the slides obtained from benignbiopsies. With the use of this threshold 91 % of malignant cases and 80% ofbenign cases could be correctly recognized. Overall, with the classificationbased on IC measurements of normal nuclei it was possible to accuratelydistinguish benign and malignant cases in 86%. The differences in nuclearfeatures characteristic of MAC could be used as a marker suggestive of occultmalignancy in the breast when only benign changes are found in the biopsy.However, the importance of this finding is not in the diagnosis of malignancy inthe breast tissue because this is very simple in the majority of cases wherecarcinoma is present in the biopsy. Much more significant is the fact that thechromatin distribution of apparently normal tissues can provide this information.Changes in morphology of normal nuclei collected from malignantbiopsies may reflect the effect of the invasive cancer cells on the neighboringtissue. Another possible answer is that the differences in nuclear featuresrepresent a field effect of hypothetical carcinogens. The frequently foundmulticentricity of breast carcinoma supports the theory of field carcinogenesis inbreast tissue (Anastassiades 1993, Ashikari 1977, Rosen 1980, Lagios 1982,Silverstein 1987, Patchefsky 1989). MAC are therefore suggestive of a higherrisk of developing malignancy: A benign biopsy with a high frequency of “MAC”nuclei would be suspicious for a high progressive potential of benign changes.It is also possible that MAC are associated with the recurrence after thelocal removal of carcinoma in situ or invasive carcinoma. This suggests that theanalysis of normal tissue may be important in the management of patients with in157situ or invasive carcinoma of the breast. However, this is only a speculationwhich must be examined in further studies by the analysis of such cases.1586. SUMMARYThe aim of this study was to obtain prognostic information, based on thenuclear morphology and nuclear DNA content, for benign breast disease andductal carcinoma in situ (DCIS). Image cytometry measurements performed ontissue sections were used to accomplish the objectives of this thesis.The adequacy of the DNA measurements performed on tissue sectionswas confirmed in comparison with other cytometric techniques. It was shownthat the nuclear DNA content measurements of tissue sections are as reliable inthe detection of aneuploidy as nuclear DNA measurements performed by flowcytometry or automated image cytometry techniques using smears or cytospins.Progressive changes in quantitative nuclear features were identified frommeasurements on different breast diseases; features changed in parallel with theincreasing risk of these diseases for the subsequent development of invasivecarcinoma. These results suggested that nuclear measurements weremeaningful for studying prognosis of breast diseases.Next, the differences in nuclear morphology and DNA content, werecharacterized between different histological types of DCIS. The nuclear DNAcontent, size, irregularity of shape and chromatin texture increased from thelowest values in cribriform type to the highest values in comedo type DCIS.Aneuploidy was demonstrated in about 60% of non-comedo DCIS and in 95% ofcomedo DCIS.An important objective was to characterize differences between pureDCIS and DCIS associated with invasion in surrounding tissue and to distinguishthese two types of DCIS on the basis of quantitative nuclear features. This was159accomplished by the detection of nuclear features of DCIS, which wereindicative of the presence of invasive carcinoma in the surrounding breasttissue. The classification system based on nuclear features was used todiscriminate between cases with pure DCIS and cases with DCIS which hadinvasive cancer in the surrounding tissue. The classification was correct in 80%of non-comedo cases and in 100% of comedo cases.Finally, the existence of malignancy associated changes (MAC) in breasttissue was demonstrated. Marker features, characteristic of MAC, were detectedin epithelial nuclei from normal appearing lobules in breasts which wereresected for carcinoma The frequency of “MAC” nuclei was low in benign tissue,increased in tissue with DCIS and was the highest in tissues with invasivecarcinoma. Based solely on the measurements of nuclei in normal appearinglobules it was possible to discriminate between patients with benign breastdisease and patients with invasive carcinoma in more than 85% of cases.In conclusion, this work has shown that nuclear measurements obtainedby image cytometry of tissue sections provide information which could be usefulfor diagnosis and prognosis in benign breast diseases or ductal carcinoma insitu (DCIS). Nuclear features could be applied as a tool to make the diagnosisof benign breast diseases more objective. Malignancy associated changes(MAC) in breast tissue could be employed as a marker for an occult malignancyin the breast and possibly as a marker of increased risk for the subsequentdevelopment of malignancy. Nuclear features of DCIS, indicative of thepresence of invasive carcinoma in the adjacent breast tissue, could be appliedas a marker for the invasive disease in the surrounding breast in cases whereonly DCIS is found in the biopsy. 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