Science, Irving K. Barber Faculty of (Okanagan)
Computer Science, Mathematics, Physics and Statistics, Department of (Okanagan)
DSpace
UBCO
Kunkyab, Tenzin
Master of Science - MSc
University of British Columbia
For early-stage and medically inoperable non-small-cell lung carcinoma (NSCLC) patients, stereotactic ablative radiotherapy (SABR) is a recommended treatment alternative to surgery, with comparable local control rates. Due to the high radiation dose delivered using SABR, patients often experience a radiation-induced lung injury called fibrosis, which can conceal a developing local recurrence on surveillance imaging scans. Identifying recurrences early and accurately is especially crucial if the patient may need an additional therapy to improve their clinical outcome. In this study, radiomics, an emerging medical image analysis tool, is investigated to determine if it can be used to accurately differentiate a local recurrence from fibrosis. This radiomics study is divided into two parts: first the radiomic features proposed in a previous research study were reevaluated in our patient dataset. The preliminary results showed that the performance of a model based on those radiomic features was not reproducible for the follow-up images from our patient set. Second, a new set of features were proposed and validated using three independent validation sets at different time points of follow-up imaging. Post-treatment computed tomography (CT) scans (n = 168) of 79 patients treated with SABR were included. The training set consisted of 24 months median follow up with 16 recurrences and 31 non recurrences (47 patients in total). Independent validation sets were separated into three cohorts by follow-up CT scans at 24 months (cohort 1), 9 â 12 months (cohort 2), and 5 â 9 months (cohort 3). The top five radiomic features selected from the training set were Dependence Non-Uniformity Normalized, Small Dependence Low Gray Level Emphasis, Run Length Non-Uniformity Normalized, Dependence Non-Uniformity and Small Dependence Emphasis. For validation cohort 1 the true positive rate (TPR) was 70% and true negative rate (TNR) was 100% (AUC = 0.85), for cohort 2 the TPR was 55.6% and TNR was 94.4% (AUC = 0.75), for cohort 3 the TPR was 70% and TNR was 90% (AUC = 0.80). Consequently, weâve demonstrated that radiomic analysis could achieve high TNR in the detection of a non-recurrence and satisfactory TPR in detecting a recurrence. External validation and further study are required to determine the generalizability of this radiomic modeling.
Radiomics for early detection of tumour recurrence in patients treated with Lung Stereotactic Ablative Radiotherapy (SABR) by Tenzin Kunkyab B.Sc., Maastricht University, 2017 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Medical Physics) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) August 2020 © Tenzin Kunkyab, 2020 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: Radiomics for early detection of tumour recurrence in patients treated with lung Stereotactic Ablative Radiotherapy (SABR) submitted by Tenzin Kunkyab in partial fulfillment of the requirements of the degree of Master of Science in Medical Physics. Dr. Derek Hyde (Irving K. Barber Faculty of Science) Supervisor Dr. Andrew Jirasek (Irving K. Barber Faculty of Science) Supervisor Dr. Jeff Andrews (Irving K. Barber Faculty of Science) Supervisory Committee Member Dr. Christina Haston (Irving K. Barber Faculty of Science) Supervisory Committee Member Dr. Sepideh Pakpour (Faculty of Applied Science, School of Engineering) University Examiner iii Abstract For early-stage and medically inoperable non-small-cell lung carcinoma (NSCLC) patients, stereotactic ablative radiotherapy (SABR) is a recommended treatment alternative to surgery, with comparable local control rates. Due to the high radiation dose delivered using SABR, patients often experience a radiation-induced lung injury called fibrosis, which can conceal a developing local recurrence on surveillance imaging scans. Identifying recurrences early and accurately is especially crucial if the patient may need an additional therapy to improve their clinical outcome. In this study, radiomics, an emerging medical image analysis tool, is investigated to determine if it can be used to accurately differentiate a local recurrence from fibrosis. This radiomics study is divided into two parts: first the radiomic features proposed in a previous research study were reevaluated in our patient dataset. The preliminary results showed that the performance of a model based on those radiomic features was not reproducible for the follow-up images from our patient set. Second, a new set of features were proposed and validated using three independent validation sets at different time points of follow-up imaging. Post-treatment computed tomography (CT) scans (n = 168) of 79 patients treated with SABR were included. The training set consisted of 24 months median follow up with 16 recurrences and 31 non recurrences (47 patients in total). Independent validation sets were separated into three cohorts by follow-up CT scans at 24 months (cohort 1), 9 â 12 months (cohort 2), and 5 â 9 months (cohort 3). The top five radiomic features selected from the training set were Dependence Non-Uniformity Normalized, Small Dependence Low Gray Level Emphasis, Run Length Non-Uniformity Normalized, Dependence Non-Uniformity and Small Dependence Emphasis. For validation cohort 1 the true positive rate (TPR) was 70% and true negative rate (TNR) was 100% (AUC = 0.85), for cohort 2 the TPR was 55.6% and TNR was 94.4% (AUC = 0.75), for cohort 3 the TPR was 70% and TNR was 90% (AUC = 0.80). Consequently, weâve demonstrated that radiomic analysis could achieve high TNR in the detection of a non-recurrence and satisfactory TPR in detecting a recurrence. External validation and further study are required to determine the generalizability of this radiomic modeling. iv Lay Summary This work attempts to tackle a major clinical challenge faced by physicians after a lung cancer patient undergoes a high dose radiation treatment. An unintended consequence of the radiation treatment is the development of scar tissue called Fibrosis, in the nearby surrounding lung tissue. A tumour recurrence that may occur after the treatment looks similar to the fibrosis, in terms of appearance and temporal evolution. From the routine medical imaging scans, we aim to extract mathematical features and see if we can identify a recurrence from fibrosis. This is especially important when the cancer recurrence is treatable, potentially prolonging survival whereas alternatively, a fibrosis may not need any additional treatment or medical intervention. v Preface The basis of this thesis initially stems from Dr. Derek Hydeâs interest in bringing radiomics to our cancer center. Radiomics has become an emerging area of research across the globe as many healthcare institutions are striving to incorporate machine learning based tools and software that can be beneficial in improving overall clinical outcome of the patient. A multidisciplinary team of medical physicist (Dr. Derek Hyde, Supervisor), radiation oncologist (Dr. Benjamin Mou), committee members (Dr. Andrew Jirasek, Dr. Christina Haston, Dr. Jeff Andrews), undergraduate students (Amanda Sambrielaz and Alexis Mckendry), Dr. Steven Thomas (Co-Investigator at BC Cancer Vancouver) have contributed in their own capacities towards completing this project. Dr. Hyde and the author (graduate student) oversaw and managed the project from its initial stage until the end. Dr. Mou contributed towards the clinical aspects of the study and advised whenever we had doubts regarding understanding of the medical reports, response assessment and about the overall problem we are aiming to solve. The committee members have always been open to questions and have given beneficial advices to tackle the challenges we faced especially in terms of data analysis. The undergraduate students reviewed the medical imaging reports and assisted us in every way possible and in timely manner. Lastly, Dr. Thomas helped immensely in the planning of this project with his initial assessment of the provincial patient data. This research was approved by a harmonized research ethics board review (The University of British Columbia and BC Cancer Agency) with the application number H18-00627. Throughout the project, the guidelines of the research ethics board were adhered to. vi Table of Contents Abstract ....................................................................................................................................................... iii Lay Summary ............................................................................................................................................. iv Preface .......................................................................................................................................................... v Table of Contents ....................................................................................................................................... vi List of Tables ................................................................................................................................................ x List of Figures ............................................................................................................................................. xi Acknowledgements ................................................................................................................................... xiii Dedication ................................................................................................................................................. xiv Chapter 1: Introduction .............................................................................................................................. 1 1.1 General overview of lung cancer ............................................................................................... 1 1.2 Lung cancers types .................................................................................................................... 1 1.3 Lung cancer symptoms & diagnosis .......................................................................................... 2 1.3.1 Overview of diagnostic evaluation ..................................................................................... 2 1.3.1.1 Diagnostic Imaging .................................................................................................... 2 1.3.1.1.1 Chest Radiography .................................................................................................... 2 1.3.1.1.2 Computed Tomography (CT) .................................................................................... 3 1.3.1.1.3 Positron Emission Tomography (PET) .................................................................... 5 1.3.1.2 Biopsy ......................................................................................................................... 5 1.4 Lung cancer staging ................................................................................................................... 6 1.4.1 Imaging in lung cancer staging ........................................................................................... 7 1.5 Overview of lung cancer treatments .......................................................................................... 7 1.5.1 Surgery ................................................................................................................................ 7 1.5.2 Radiotherapy ....................................................................................................................... 8 1.5.3 Other Treatment Modalities for Lung Cancers ................................................................. 10 1.6 Overview of the comparison between SABR vs Surgery ........................................................ 10 vii 1.7 Response assessment in post-SABR patients .......................................................................... 11 1.7.1 Local Recurrence .............................................................................................................. 11 1.7.2 Radiation-Induced Lung Injury (RILI) ............................................................................. 11 1.7.3 Clinical challenges to detect a local recurrence ................................................................ 12 1.7.4 Mode of recurrence detection ........................................................................................... 12 1.7.4.1 Biopsy ....................................................................................................................... 12 1.7.4.2 PET Imaging ............................................................................................................. 13 1.7.4.3 CT Imaging ............................................................................................................... 13 1.7.4.4 Radiomics ................................................................................................................. 14 Chapter 2: Theory of Radiomics .............................................................................................................. 16 2.1 Introduction ............................................................................................................................. 16 2.2 Data selection .......................................................................................................................... 18 2.3 Medical Imaging ...................................................................................................................... 19 2.3.1 Segmentation ..................................................................................................................... 19 2.3.2 Imaging at multiple points ................................................................................................ 20 2.4 Feature extraction .................................................................................................................... 20 2.4.1 Standardization of radiomics pipeline ............................................................................... 20 2.4.2 Texture feature and analysis in Radiomics ....................................................................... 20 2.5 Feature harmonization ............................................................................................................. 22 2.6 Analysis ................................................................................................................................... 23 2.6.1 Feature selection ............................................................................................................... 23 2.6.2 Modelling and classification ............................................................................................. 23 Chapter 3: Pilot Study: Evaluation of previously reported radiomic features in a new patient dataset ......................................................................................................................................................... 26 3.1 Introduction ............................................................................................................................. 26 3.2 Material and Methods .............................................................................................................. 27 viii 3.2.1 Creating a database with follow-up images for lung SABR patients ........................... 27 3.2.2 Patient selection for pilot study .................................................................................... 27 3.2.3 GrowCut Segmentation ................................................................................................ 29 3.2.4 Feature extraction using pyradiomics ........................................................................... 30 3.2.5 Analysis using radiomic features from previous research ............................................ 30 3.2.6 Feature Selection .......................................................................................................... 30 3.2.7 Classification using Support Vector Machine .............................................................. 32 3.3 Results ..................................................................................................................................... 32 3.4 Discussion ................................................................................................................................ 34 3.5 Conclusion ............................................................................................................................... 35 Chapter 4: Principal study: Radiomics study incorporating a harmonization process to include CT images with diverse image acquisition parameters ................................................................................ 36 4.1 Introduction ............................................................................................................................. 36 4.2 Material and Methods .............................................................................................................. 37 4.2.1 Baseline patient characteristics ......................................................................................... 37 4.2.2 Training and test set .......................................................................................................... 39 4.2.3 Segmentation and feature extraction ................................................................................. 40 4.2.4 Feature harmonization and selection ................................................................................. 40 4.2.5 Classification using Support Vector Machine .................................................................. 41 4.3 Results ..................................................................................................................................... 41 4.4 Discussion ................................................................................................................................ 43 4.5 Conclusion ............................................................................................................................... 46 Chapter 5: Conclusion .............................................................................................................................. 47 5.1 Overview of the research projects ........................................................................................... 47 5.2 Summary of the chapters (2 and 3) .......................................................................................... 47 5.3 Limitations ............................................................................................................................... 48 ix 5.4 Future Work ............................................................................................................................. 49 Bibliography .............................................................................................................................................. 51 Appendices ................................................................................................................................................. 61 Appendix A ........................................................................................................................................ 64 Appendix B ......................................................................................................................................... 65 Appendix C ......................................................................................................................................... 61 Appendix D ........................................................................................................................................ 65 Appendix E ......................................................................................................................................... 66 Appendix F ......................................................................................................................................... 68 x List of Tables Table 1. High-risk features and their sensitivity, specificity at the months of detection.68 (Retrieved from High-risk CT features for detection of local recurrence after stereotactic ablative radiotherapy for lung cancer Huang, K. et al..) .............................................................................................................................. 14 Table 2. The baseline characteristics of the patients chosen in the pilot study. .......................................... 28 Table 3. Baseline patient characteristics of patients (n=79) used in the principal study. ........................... 38 Table 4. Breakdown of scanner vendors, models and reconstruction kernel used in the principal study. .. 39 Table 5. Selected radiomic features and the percentage of votes. ............................................................... 42 Table 6. The misclassified patient anonymized ID, the follow up scan (misclassified = X, accurately classified = (â) and the findings from medical imaging reports. ............................................................... 44 Table 7. The previously found radiomic features and its corresponding implementation in pyradiomics for direct comparison. ....................................................................................................................................... 65 Table 8. Radiomic features and formula selected using the leave-one-out approach. ................................ 66 Table 9. Radiomic features, formula, percentage of votes and description. ............................................... 68 xi List of Figures Figure 1. (a) A simplified diagram of chest radiography and (b) Computed Tomography (CT) imaging. .. 3 Figure 2. A simple back projection (Adapted from Bushberg et al.)15 ......................................................... 5 Figure 3. Top figures (a) illustrate the morphological changes in appearance of a recurrence and bottom figures (b) illustrates a non-recurrence patient at 0, 3, 6, 18+ months follow up CT images. .................... 12 Figure 4. Illustrates the conventional model vs radiomics model. (Adapted from Morin et al.).84 ............ 18 Figure 5. Radiomics workflow (Adapted from Aerts et al.).102 ................................................................. 19 Figure 6. Forward Feature Selection ........................................................................................................... 23 Figure 7. Support Vector Machine. ............................................................................................................. 24 Figure 8. GrowCut segmentation algorithm available in the 3D Slicer software. The GrowCut algorithm requires two input; approximate region of interest (green) and boundary (yellow). The final segmentation is produced using a cellular automaton algorithm. ..................................................................................... 30 Figure 9. The schematic diagram of leave-one-out cross validation approach. .......................................... 31 Figure 10. Counting radiomic features based on leave-one-out cross validation, at each fold all the features are counted. .................................................................................................................................... 31 Figure 11. Illustrates the radiomic performance of the features found in previous research (Mattonen et al.), an internal cross validation using 3-folds, 5-folds, 10-folds, leave-one-out was performed. .............. 33 Figure 12. Illustrates the radiomic performance of the new features found using the feature selection method explained in this chapter. ................................................................................................................ 33 Figure 13. Schematic classification of training and test set. ....................................................................... 40 Figure 14. (a.) shows an unharmonized radiomic feature value and (b.) shows "harmonized" radiomic feature value based on the scanner vendors (0 â non-recurrence, 1 â recurrence). .................................... 41 Figure 15. (a.) shows the image matrix, in (b.) GLCM is calculated using the highlighted cooccurrences (i.e. 4 and 1, 3 and 4) in the image matrix. .................................................................................................. 61 Figure 16. (a.) is the image matrix and (b.) is the GLSZM matrix calculated using highlighted size zones. ..................................................................................................................................................................... 61 xii Figure 17. (a.) shows the image matrix and (b.) shows the calculated GLRLM using highlighted run lengths. ........................................................................................................................................................ 62 Figure 18. (a.) shows image matrix, (b.) shows the dependence matrix calculated using the values inside the box in image matrix (a.) ........................................................................................................................ 62 Figure 19. (a.) shows the image matrix I with NGTDM calculated in (b.) ................................................. 63 xiii Acknowledgements It is with utmost pleasure that I acknowledge the assistance and support of my graduate supervisor Dr. Derek Hyde who continuously supported me throughout my masterâs research. Thank you for taking me on as your first ever graduate student and the opportunity to pursue an incredibly exciting and relevant research relating to radiomics. It has been my pleasure working with you for the last year and half. I would also like to thank all of my committee members, Dr. Andrew Jirasek, Dr. Jeff Andrews, Dr. Christina Haston and Dr. Benjamin Mou (radiation oncologist) for your continuous guidance and support without which I wouldnât be able to accomplish this work. I would also like to thank Dr. Mattonen (The University of Western Ontario) and her student Salma (PhD candidate) for their advice in reproducing their study. I am grateful for all of your advice. xiv Dedication First and foremost, I would like to thank my parents for their unconditional love and guidance showered upon me every day, my four sisters for being my backbone and supporting me through all the challenges and tough times of my life. Without the support of my family, I donât think I would be in the position of pursuing my own academic research in a well-known academic institution. I would like to thank the Horizon Foundation (especially Patrick Smulders, Kirkland Smulders and Louise Garrett) & the Dalai Lama Trust for supporting my undergraduate and graduate education. Last but not the least, I would like to give my big thanks to all my friends, faculties and all the other people that Iâve met along the way throughout my short personal journey. I owe you all everything. I would like to dedicate this thesis towards my late aunt Choenyi Sangmo, who sadly passed away due to advanced stage breast cancer in November of 2018. My mother and I have always kept you in our daily prayer ever since. 1 Chapter 1: Introduction This chapter introduces lung cancer, diagnosis and treatment techniques, particularly focusing on stereotactic ablative radiotherapy (SABR). The clinical challenges associated with post-SABR response assessment and the purpose of this study is discussed. 1.1 General overview of lung cancer The leading cause of deaths due to cancer in both genders in Canada is lung cancer, responsible for approximately 26% of cancer deaths.1 The five-year net survival rate in patients with lung cancer remains low, of only around 19%.1 In 2020, it is predicted that there will be 29,800 Canadians with a positive lung cancer diagnosis (13% of all novel cancer cases) and 21,200 deaths (25% of all cancer deaths).1 Smoking, especially tobacco, is the most significant and widely known risk factor associated with lung cancer.1 Around 72% of all lung cancer cases in Canada are predominantly caused by smoking.2 The incidence rate of lung cancer in men is higher than in women, possibly due to the differences in tobacco use.1 The likelihood of developing lung cancer increases with the total number of years using tobacco (mainly cigarettes), and age at the start of smoking.1 There are other known risk factors, for instance, second-hand smoke, environmental and occupational factors such as air pollution, radon exposure and asbestos.2â5 Possible risk factors are those factors that may have links with lung cancer but insufficient evidence to confirm they are a known risk factor, which include low consumption of vegetables and fruits in diet, lack of physical inactivity, and smoking cannabis.6â8 1.2 Lung cancers types Lung cancer is divided into two major histological groups: small cell lung carcinoma (SCLC, represents 15% of all lung cancers) and non-small cell lung carcinoma (NSCLC, represents 85% of all lung cancers).9 SCLC is commonly found in the bronchi in the central region of the lungs. SCLC cells are small (9.2 ðð in diameter)10; nevertheless, they are one of the most invasive types of lung cancer that can spread rapidly to other parts of the body. It is most often found in populations who have smoked tobacco. Non-small cell lung carcinoma (NSCLC) type, on the other hand, encompasses the majority of lung cancer. It occurs in the epithelial cells of the lung tissue while typically arising in the central bronchi and extending to the alveoli cells. The NSCLC can be further divided into adenocarcinoma, squamous cell carcinoma, and large cell carcinoma as the most common variants. Cancers can also metastasize in the lung and the most common types are breast and colorectal. Lung metastases are sometimes known as secondary cancer, which is different from primary cancer that originates in lungs. However, the secondary lung cancers are made up of the same type of cells as the 2 primary cancer cell (the origin), for instance, breast cancer cells give rise to breast cancer cells in the area of lung where it is metastasized. 1.3 Lung cancer symptoms & diagnosis The majority of lung cancers arise in central airways causing post-obstructive pneumonia or lymph node enlargement leading to coughing, which is the most common symptom of lung cancer.11 Other symptoms include experiencing chest discomfort, hemoptysis, dyspnea, loss of appetite, and weight loss.11,12 However, these symptoms are nonspecific and may indicate other conditions such as coronary obstructive pulmonary disease (COPD), thus, further tests are required to accurately detect lung cancer.13 1.3.1 Overview of diagnostic evaluation Diagnostic tests or diagnosis refers to the process of identifying the underlying cause of a disease or a health issue. The initial evaluation of a lung cancer diagnosis is generally performed with a personal history and physical examination.14 Personal history includes health records of the patientâs symptoms, smoking habit, exposure to different carcinogenic substances, and past medical events (for instance previous radiation therapy). Physical examination typically focuses on relevant body systems such as an examination of the respiratory and lymphatic systems. Investigations comprises of several important tests such as complete blood cell count, blood chemistry test, electrolytes, blood urea nitrogen and chest imaging (explained in the diagnostic imaging section).14 Other crucial tests in clinical evaluation includes sputum cytology and invasive techniques such as biopsy. The diagnostic evaluation of a lung cancer comprises of numerous methods that can be broadly categorized into the following categories: 1.3.1.1 Diagnostic Imaging 1.3.1.1.1 Chest Radiography Chest radiography, or commonly known as X-ray, uses low doses of radiation to produce a 2-D image of the body on a film or flat panel detector. Chest radiography is frequently performed as an initial imaging test to confirm the presence of a suspected lung cancer, therefore, playing a critical role in lung cancer diagnosis.11 In asymptomatic patients, abnormalities in chest radiography can provide the first indication of the existence of lung cancer and patients with symptoms may benefit from radiography in determining the presence of a primary tumour.11 Chest radiography can also be beneficial for diagnosis of patients who have non-specific symptoms or symptoms suggestive of a metastatic disease. 3 1.3.1.1.2 Computed Tomography (CT) Computed Tomography (CT) is another X-ray based technique where a narrow beam of radiation is directed at a patient, which then rapidly rotates around the patient producing cross-sectional images of the body (figure 1). These images are also known as âslicesâ or tomographic images that contain more information than a 2-D chest radiograph. The X-ray tube emits a radiation beam around the patient from different angles results in a projection data, from which the tomographic images are obtained. The mathematical process of converting those acquired projection data into CT images is called image reconstruction. Reconstruction algorithms include simple back projection, filtered back projection, Fourier-based and iterative reconstruction. Simple back projection is a technique that simply âsmearsâ back the projection data onto image space, thereby reconstructing the image. The concept of simple back projection can be explained using figure 2. In figure 2, the projection data is represented by a and b, which is the attenuated X-ray that has passed through the patient. As shown in the figure, the projection data is the linear sum of the voxels along the ray. The final objective of the simple back projection algorithm is to obtain the values inside the image (inside the box) using the projection data a and b.15 Figure 1. (a) A simplified diagram of chest radiography and (b) Computed Tomography (CT) imaging. The process of simple back projection along large number of angles around 360° results in 1/r blurring of the image.15 The filtered back projection algorithm uses a convolution filter (kernel) to reduce the blurring associated with the simple back projection, producing sharper images. The convolution process described this reconstruction algorithm can be considered as a form of a mathematical filtering, therefore it is called a filtered back projection.15 4 The Fourier-based method enables CT image reconstruction by transforming and analyzing the data in the frequency domain rather than spatial domain, which is computationally efficient. The concept of convolution described in the previous paragraph can be performed computationally faster using a Fourier transform.15 Therefore, this Fourier-based reconstruction algorithm are often implemented in commercial scanners.15 Iterative reconstruction starts with an approximate CT image, a series of updates (iterations) are implemented to obtain a more accurate representation of the scanned structure.15 The first âapproximateâ image can be filtered back projected image. The fundamental process underlying iterative reconstruction is to minimize the difference between the acquired projection data and the forward projection data using the âapproximateâ image.15 The equipment manufacturers have different reconstruction kernel settings available with their CT scanner. Depending on the application, the radiologists/technologists can choose reconstruction kernels from the variety of settings on CT scanners. Although CT and chest radiography both use X-rays, CT scans are more likely to detect the presence of lung cancer and often more conclusive than the chest radiographic scans.16 Furthermore, CT scans are widely used to determine the size, shape and location of a suspicious nodule. A suspicious nodule is typically represented by a rounded or irregular structure of increased density (attenuation). The advantage of CT scan over chest radiography is that it provides higher resolution (pixel size 0.5 to 1 mm in all spatial directions) 3-D images that can be viewed in any image plane.17 Contrast-enhanced CT imaging (with intravenous contrast agent) is widely used to more easily delineate organs and structures of similar densities on a regular CT scan. For instance, they are used to show enlarged lymph nodes that might have resulted due to the spread of centrally located cancers to the mediastinum or to differentiate mediastinal lymph nodes from vasculature.18 5 1.3.1.1.3 Positron Emission Tomography (PET) Positron Emission Tomography (PET) is a type of nuclear medicine technique that generates images based on the distribution of positron-emitting nuclides inside the patient.15 During a PET imaging procedure, a substance labeled with a radioactive atom, such as fluorodeoxyglucose (FDG), is injected into the body. In the case of FDG, it is localized in high metabolically active areas, such as cancer cells which use a large amount of energy. 18F-FDG or 2-deoxy-2-[fluorine-18] fluoro-D-glucose in particular, is a widely used radionuclide that has a variety of applications in oncology.19 As the radionuclide decays, a positron is emitted and the subsequent annihilation with an electron and two photons are emitted in opposite directions. Photon detection enables the localization and measurement of the metabolic activities of the cancerous cells. Standardized Uptake Value (SUV), a commonly used semiquantitative measure, represents the amount of uptake of the radionuclide tracers (generally a SUVmax greater than 2.5 is linked to a suspicious malignancy).20, 21 Nearly all PET systems that are currently produced are coupled to an X-ray CT system, therefore, they are often referred to as âPET/CTâ system.15 In general, if the findings after a non-contrasted CT scan is inconclusive, a contrast-enhanced CT and/or a PET scan is performed for confirmation of the presence of the disease.14 1.3.1.2 Biopsy For patients who have suspected lung cancer based on radiographic and clinical laboratory results, a biopsy may be performed to confirm the presence of a cancer. A biopsy is a procedure where a small tissue sample from the body is removed for examination under a microscope. The choice of biopsy technique is decided based on the location, type, and size of the lung tumour.14 The risk level of biopsy increases with the size of the needle used and the location of the biopsy target.22 In general, the least invasive biopsy procedure capable of acquiring an adequate tissue sample is utilized.23 Figure 2. A simple back projection (Adapted from Bushberg et al.)15 6 Conventional bronchoscopy performed using a bronchoscope (a thin tube used for viewing inside the lungs) through the main airways of the lungs is suited for central lesions. For peripheral lesions, CT guided thoracic needle aspiration is a preferred method.14 For this type of biopsy, the needle is inserted through the chest wall and is guided by a CT scan to the area where the lung tumour is present. Endobronchial ultrasound24 and electromagnetic navigation25 are techniques that provide high success rates in assessing patients with peripheral lesions where it is often difficult to obtain tissue samples due to the location of the tumour.24, 25 Lastly, the diagnostic evaluation often involves a multidisciplinary team of physicians which may include thoracic surgeons, pathologist, radiologists and respirologists. The results ultimately decide the treatment path and determines the prognosis of the lung cancer. After the diagnosis is confirmed by biopsy, lung cancer staging is naturally the following step. 1.4 Lung cancer staging Lung cancer staging is an essential step to serve the five following purposes: a.) help the multidisciplinary team of physicians plan an appropriate treatment b.) determine some indication of the clinical outcomes c.) assist in the evaluation of the treatment results d.) aid the exchange of information between different cancer centers e.) advance the research of lung cancer.26 Staging of the disease is necessary because lung cancer stage is directly associated to the survival of the patient and it is the main factor in determining the outcome of the treatment.27 There are two types of staging: Clinical and Pathologic. Clinical staging involves parameters obtained before the treatment such as physical examination, laboratory data, and medical imaging. The latter part of clinical staging involves bronchoscopy, esophagoscopy, mediastinotomy and thorascopy, all of which may include biopsies.27 Pathologic staging comprises of observing the tissue sample under a microscope obtained using invasive medical intervention such as surgical resection. The findings from pathologic staging may change the results from clinical staging (which includes evaluation using imaging modalities).14 Pathologic staging is a better predictor of the prognostic outcome than clinical staging.27 In general, lung cancer staging is performed using the TNM system (Tumour, Nodes, Metastasis system). As the terms suggest, the T category describes the size and the extent of the primary tumour. The N category defines extent of the tumour to the regional lymph nodes. Finally, the M category determines the presence and extent of distant metastasis. The data from TNM staging is used to obtain the four primary group stages of the tumour. In NSCLC, there are four main stages of cancer; stage I cancers are considered to be early-stage lung cancers with tumour size < 5 cm, stage II are the ones that have chest 7 wall invasion, pericardium or have separate tumour nodule (s) in the same lobe, stage III are locally advanced that have spread to mediastinum and stage IV are those cancers that are metastasized outside of the lung.28 Generally, if the stage is lower in number, the cancer has spread less. SCLC staging can be described with TNM staging but more often it is described using a two stage system: limited if the cancer is confined to the hemithorax, the mediastinum or the supraclavicular lymph nodes; extensive when it extends beyond the supraclavicular nodes.26 1.4.1 Imaging in lung cancer staging CT imaging is an essential tool in staging lung cancer. CT images may be used to describe the invasiveness of the tumour.29,30 Multiple studies have found CT features that are predictors that can be linked to the prognosis.31 These reported features include lobulated margins, air bronchograms and pleural tags.31 However, chest wall invasion is particularly challenging to find on CT images and may not be definitive. The sensitivity and specificity of finding nodal metastasis on CT images has been reported as 52-64% and 62-69% respectively.32,33 For distant metastasis, the yield of imaging for extrathoracic sites are low in the absence of other clinical parameters such as laboratory tests, but CT imaging is recommended when patients have stage III disease with negative clinical evaluation.27 PET imaging is also an invaluable technique in lung cancer staging. PET imaging in itself is more accurate than CT in identifying mediastinal adenopathy and is more accurate in determining lung cancer stages for NSCLC.27 Diagnostic accuracy of 79% and sensitivity of 64% were reported for lung cancer staging with a 18F-FDG PET/CT system.34 To summarize, PET/CT is regarded as the gold standard imaging modality for the purpose of lung cancer staging.35 1.5 Overview of lung cancer treatments 1.5.1 Surgery Surgery, with curative intent, is the recommended treatment for early-stage (stage I and II) NSCLC36,37 provided that the patient has no contraindication to surgical intervention. Approximately 30% of early-stage NSCLC patients are resectable and functionally operable.38 Five-year net survival rates of patients treated with surgery are reported as 60%-80% for stage I NSCLC and 30%-50% for stage II NSCLC patients.36 Following are some broad classifications of surgical techniques: a) Lobectomy: a surgical procedure where the lobe of lung along with the whole tumour is removed. Since the outcomes of the randomized Lung Cancer Study Group trial was published in 1995, lobectomy is established as the standard of care for early-stage NSCLC.39 The study found that lobectomy offers a lower locoregional recurrence and a 8 lower mortality rate than limited resection.39 A lobectomy is also recommended in patients who have solid-type stage I NSCLC.40 b) Wedge resection: For patients who donât have decent lung function, wedge resection (a type of sublobar resection) may be used as an alternative where the tumour along with some healthy lung tissue is removed.41 Anatomical segmentectomy (another type of sublobar resection) is a preferred treatment over wedge resection due to reduced rates of locoregional recurrence in stage IA NSCLC.42 c) Sleeve resection: a surgical option in which the tumour is removed from the bronchus of the lung.43 d) Pneumonectomy: Pneumonectomy is a technique where a whole lung is removed from the patient. e) Minimally invasive surgery such as Video-assisted thoracic surgery (VATS) and robot-assisted thoracic surgery (RATS) are also other forms of widely accepted treatment techniques for early-stage NSCLC.41 The role of surgery in SCLC is limited.44 Local treatment of SCLC has been associated with low survival rates. The survival rates reported by a study in 1969 by British Medical Research Council comparing surgery to radiotherapy in SCLC patients were 4 and 10% at 24 months, 3 and 7% at 48 months, and 1 and 4% at 60 months respectively.45 The study showed that survival rates are low for either single treatment modality. A systematic review reports that the current standard treatment for limited stage SCLC is chemoradiation (combining chemotherapy and radiation therapy). 46 Multimodality treatment for early-stage SCLC is recommended and shown to increase the survival rates considerably over a single treatment modality.44 1.5.2 Radiotherapy Radiation therapy, or radiotherapy, uses high-energy radiation beams to damage the cells in the irradiated volume; resulting in DNA damages that causes cell death. This type of technique helps control the tumour at specific sites or organs inside the body.47 Patients with NSCLC who are medically inoperable (due to presence of comorbidities, complications or personal choice) may benefit from radiotherapy.47 Radiotherapy may also be used as a palliative treatment to improve the quality of life of the patients who have a poor response to surgical or other types of treatments.47 External beam radiotherapy can be broadly classified into two main techniques: 9 a) Conventional radiotherapy: This type of external beam radiotherapy technique delivers 1.8-2Gy per day to a lung cancer patient over 6-7 weeks, with total of dose around 60 Gy.48 The clinical outcomes associated with conventional radiotherapy were not as good as surgery, therefore radiotherapy was initially considered as a substandard alternative to surgery for early-stage lung cancers.49,50 b) Stereotactic ablative radiotherapy (SABR) is another form of external beam radiotherapy therapy in which high doses of radiation are delivered in few number of fractions to destroy a small tumour, achieving high local control rates.37 SABR techniques accurately localize the lung tumour and ensure precise positioning to enable a delivery of radiation treatment with an ablative dose.47 There are several important advantages of SABR over conventional radiotherapy. Since the radiation schemes of SABR and conventional radiotherapy are different, i.e. dose per fraction, the efficacy of the two treatments is compared using biologically effective dose (BED).51 BED is a measure of effective biological dose delivered by a combination of dose/fraction and total dose to a particular tissue.51 BED is used to compare different fractionation regimes in radiation therapy.52 BED represents the physical dose needed for a particular effect if the dose were to be delivered in infinitely small dose/fraction. A gain in BED is correlated to increase in tumour control probability (TCP).53 BED is calculated as follows ðµð¸ð· = ð â ð *1 + ðð¼/ð½0 where n is number of fractions, d is the dose/fraction and ð¼/ð½ is known as the alpha/beta ratio of the tissue. The parameter ð¼, ð½ characterizes the radiosensitivity of the cells that are irradiated. The ratio of the parameters ð¼/ð½ is a measure of sensitivity of the cells towards a fractionation scheme. A cell with high ð¼/ð½ is less sensitive to the effects of fraction size and more sensitive to the total dose. A high ð¼/ð½ also corresponds to early responding tissue/tumour, whereas a low ð¼/ð½ ratio corresponds to late responding tissue/tumour to the radiation.54 By calculating BED from the parameters, conventional radiotherapy has a BED of 70-80 Gy whereas SABR delivers a BED of >100 Gy, therefore, enabling SABR to have a superior biologic potency and higher tumour cell kill, resulting in better tumour control.55 The other benefit of SABR is that the radiation beams are highly focused to the tumour, covering the area from different angles with a steep dose gradient outside the tumour region into the nearby healthy tissue, thereby reducing radiation toxicity.51,56 Higher dose per fraction also means that SABR treatment can be done in fewer treatment visits over a period of a week, compared to conventional radiation therapy which is delivered with smaller dose per fraction for up to seven weeks (33 treatment visits).57,58 Hence, using SABR over conventional radiotherapy improves patient convenience during the radiotherapy treatment. 10 The challenges associated with implementing SABR are mostly related to the movement of the tumour due to the respiratory motion. These difficulties could be addressed using: a.) a secure immobilization device during treatment, usually abdominal compression plate or a belt to minimize diaphragm motion b.) accurate tumour localization with image-guided verification c.) motion management techniques such as gating, tracking, or an internal target volume approach to ensure the radiation beam is accurately targeting the tumour.57 1.5.3 Other Treatment Modalities for Lung Cancers The treatment modalities are not merely limited to radiotherapy and surgery, other treatment methods for lung cancer include chemotherapy, targeted therapy (drugs such as gefitinib) and immunotherapy. Chemotherapy uses chemical drugs to treat cancer by blocking or slowing down rapidly proliferating cells (commonly used drugs such as cisplatin and gemcitabine).59 Targeted therapy, on the other hand, are those drugs that target certain genes or proteins specific to cancer cells that stimulate cancer cell growth (for instance, EGFR gene inhibiting drugs).60 Immunotherapy is a type of treatment which helps the patientâs own immune system to specifically destroy cancer cells (for instance, pembrolizumab for NSCLC).47 Note that all of the treatment modalities discussed until now may be used to manage many stages of lung cancer. However, in this study, the SABR technique which is used for early-stage lung cancer patients (or some metastasis) is the focus of this thesis. 1.6 Overview of the comparison between SABR vs Surgery A Randomized Controlled Trial (RCT) is a way to compare the true effectiveness of two different treatment modalities. Three randomized trials to compare SABR and different surgical techniques were initiated, but due to poor accrual (<5% of planned patients joined the study), these studies were terminated early.61 There is currently a lack of phase III RCTs in comparing these two treatment methods.37 Retrospective and observational studies serve to be the most suitable approach to compare these techniques. Surgery is the gold standard and guideline established treatment for early-stage lung cancer patients who are medically operable. High local control rates are associated with surgery; more than 90% reported in many studies.62 However, in the patients who were treated with surgical resections, the most common cause of mortality rate is cancer recurrence. The rate of recurrence after a surgical resection is reported as 30-55%, with a majority of the patients having a distant metastasis.63 11 Conversely, many studies have consistently reported that SABR achieved high local control rates of 88% to 96% at 2 to 4 years based on follow-up imaging, 62,64â67 with outcomes comparable to surgical resection. In terms of survival rate, a phase II multicenter trial conducted on 55 early-stage NSCLC patients reported that the patients treated with SABR had a 3-year survival rate of 55.8%, with moderate radiation induced morbidity.68 In the light of these promising outcomes, SABR is now widely considered to be a standard treatment alternative to surgery for early-stage NSCLC cancer patients who are medically inoperable. Moreover, SABR provides reduced rates of treatment induced morbidity and increased patient convenience.47 1.7 Response assessment in post-SABR patients After SABR, a response assessment includes CT imaging at 3 months, 6 months, 12 months, 18 months, 24 months and then yearly until 7 years from the SABR treatment (BC Cancer protocol). In addition to CT imaging, a personal history and physical examination are often included. Follow-up with CT imaging serves 3 major objectives: detection of local recurrence, regional recurrence, and new primary tumours (typically occurs 2-3% per year for this patient population).69 A highly suspicious CT finding may be followed up with FDG-PET/CT (fluorodeoxyglucose or FDG, a commonly used radiotracer) if available. Finally, a biopsy may be performed if both the CT and FDG-PET findings are inconclusive or to confirm the suspicion of a disease. The main criteria for a biopsy procedure are that the patient must be medically operable and the procedure chosen must be relatively safe.70 After a local and/or a regional recurrence is confirmed, the patient ultimately may be required to have additional treatment or salvage therapy. 1.7.1 Local Recurrence A local recurrence is generally described as a tumour growing in the same region as the primary tumour (including irradiated area or the Planning Target Volume (PTV)), but may also be described as a growing lesion within the involved lobe.70 The local recurrence rate in lung cancer patients treated with SABR is expected to be about 10%.71 1.7.2 Radiation-Induced Lung Injury (RILI) Radiation Induced Lung injury (RILI) is an unintended side effect of delivering dose to the tissues surrounding the treated volume, which contains the tumour. Two common forms of RILI are pneumonitis and fibrosis. RILI is generally classified as radiation pneumonitis in the acute setting (less than 6 months) and radiation fibrosis in the late setting (beyond 6 months). About 62% of treated population experience. 12 RILI in the acute setting and 91% of patients in the late setting.71 Fibrosis can evolve even after 2 years after the treatment with no major clinical symptoms. 1.7.3 Clinical challenges to detect a local recurrence Fibrosis, which is a formation of permanent scar tissue as a result of tissue healing, can have the morphologic appearance and time course of a local recurrence in a routine follow-up CT image.72,73 This poses a difficult clinical problem: early and accurate detection of a local recurrence and differentiating it from fibrosis. In general, after CT findings indicate a suspicion of a presence of a local recurrence, FDG-PET and biopsy are the succeeding steps.71 However, since routine CT follow-up imaging is more frequently performed in the initial months after the treatment, it is important that CT findings identify patients with a local recurrence as early and as accurately as possible. It is especially true for the subset of the patients who are healthy enough to endure an additional treatment for a better clinical outcome. Inaccurate classification of a recurrence as fibrosis (false negatives) can delay the treatment of a recurrent cancer and classification of fibrosis as recurrence (false positive) can lead to unnecessary medical interventions and possibly an additional, unnecessary treatment.74,75 Figure 3 shows two patients selected from the current study with a recurrence and a fibrosis, illustrating the similar morphological changes in appearance of these two conditions. Figure 3. Top figures (a) illustrate the morphological changes in appearance of a recurrence and bottom figures (b) illustrates a non-recurrence patient at 0, 3, 6, 18+ months follow up CT images. 1.7.4 Mode of recurrence detection 1.7.4.1 Biopsy As mentioned in the previous sections, biopsy is a technique that is used to characterize the underlying pathology of the tissue, for instance, a tumour recurrence. Biopsy is the gold standard to confirm a 13 recurrence, however there are associated risk to the patient. Some of the most serious complications that may occur during a biopsy procedure include a pneumothorax and bleeding. 1.7.4.2 PET Imaging PET imaging is often used in post-SABR response assessment. In particular, FDG-PET/CT is reported to have better diagnostic accuracy in identifying a recurrence over diagnostic CT imaging.35 However, benign lung injury can also give rise to a momentary increase in the FDG avidity, resulting in a false positive diagnosis.71 Such a false positive diagnosis can lead to an unnecessary biopsy of a benign inflammatory response in the tissue.76,77 Widely varying results were reported in terms of post treatment SUVmax value, some reports indicate SUVmax as low as 3.2 and 4.2 indicate a presence of progressive disease (however not all patients in the study have a pathologically proven local recurrence), whereas in general, SUVmax > 5.0 is believed to be the most indicative of a local recurrence.78â81 Acute inflammatory response after SABR can result in increased metabolic activity that can falsely suggest a malignancy.29,82 Although PET/CT may be more accurate than CT, it is also more expensive and is not routinely available in many hospitals.71 1.7.4.3 CT Imaging Response Evaluation Criteria in Solid Tumours (RECIST) has been the standard procedure for evaluating the treatment response in oncology based on CT imaging. RECIST relies on the diameter of the region of interest alone to classify a recurrence, which may not be a reliable technique for recurrence detection when there is an increasing mass of fibrosis.71 The RECIST measurements are usually performed in the plane of image acquisition and quite often when the tumour is irregularly shaped, those measurements are subject to inter and intra-observer variability.83 A systematic review has reported certain radiographic changes in CT called the high-risk features (HRFs) that are predictive of recurrence.78 Table 1 shows the high-risk features and their sensitivity and specificity of detecting local recurrence and fibrosis. In a blinded study, HRFs were assessed using CT datasets from a pathology-proven recurrence and fibrosis. The HRFs with highest performance were enlargement after 12 months, with sensitivity of 100 and specificity of 83 respectively (Table 1). Even though the HRFs may provide an accurate detection of recurrence to some extent, HRFs may be subject to interobserver variability and may only become apparent after a year post-SABR treatment.70 Therefore, early and accurately detection of local recurrence with high-risk CT features is often challenging for the physicians. 14 Table 1. High-risk features and their sensitivity, specificity at the months of detection.70 (Retrieved from High-risk CT features for detection of local recurrence after stereotactic ablative radiotherapy for lung cancer Huang, K. et al..) High-risk features Time (months) Sensitivity Specificity Cranio-Caudal growth 13 92 83 Enlarging opacity 15 92 67 Sequential enlargement 19 67 100 Linear Margin disappearance 20 42 100 Loss air bronchogram 20 67 96 Enlargement after 12 months 22 100 83 Bulging margin 22 83 83 In summary, most modes of recurrence detection explained earlier are mainly qualitative. Quantitative methods, such as radiomic analysis, are an emerging field of study and have promising potential in various areas of studies and will be described in the next sections. 1.7.4.4 Radiomics The term âradiomicsâ was first coined in 2012 as a scientific study of imaging feature analysis in medicine.84 Radiomics can be defined as a quantitative image feature analysis tool where a large number of image features are extracted from medical images. Those extracted radiomic features can be used to reflect the underlying pathophysiological information of the tumour or the healthy tissue.85 The fundamental hypothesis of radiomics is that there is more information present in the medical images than what our visual assessment can comprehend, and therefore, the pathophysiological information of tumour can be captured in a higher-level macroscopic view using quantitative image features calculated on the medical images.85 Since 2012, a substantial number of radiomics studies have been published including a wide range of applications including tumour classification and determining the phenotype of the tumour.85 Some applications of radiomics may also include determining the prognostic outcomes of patients.85 Other major applications of radiomics include classifying the subtypes of cancers, differentiating a cancer from benign tumour.86 Radiomics studies are not be limited to one tumour site, with wide variety of applications in different sites including head and neck, lung, breast, liver and brain.87 Radiomics may also be applied in other applications outside the realm of oncology such as, in identifying the presence of ischemic strokes.88 15 As previously mentioned, the post-SABR response assessment currently relies on qualitative high-risk CT features and FDG-PET, both of which may take a year after the treatment to accurately detect a local recurrence. Accurately detecting a local recurrence early is especially essential for the subset of patients who may benefit from an additional therapy. Misclassification of a fibrosis may expose the patients to risky medical interventions and treatments, which can be avoided if accurately detected. Therefore, the hypothesis of the current thesis is that radiomics can be utilized in a clinical decision support tool (which is non-invasive and easy to use) to accurately differentiate a local recurrence from fibrosis. Thus, the application of radiomics to this clinical challenge is highly relevant and urgently needed in a clinical workflow. A radiomics study published in 2015 by Mattonen et al. showed that the radiomic features extracted from CT images may be a suitable tool in detecting a local recurrence, while accurately distinguishing it from fibrosis at an early follow up. The thesis is divided into a pilot and principal study. The objective of the pilot study can be explained in twofold; firstly, to reproduce the study published by Mattonen et al. and evaluate their radiomic features in a new dataset. Secondly to select five radiomic feature that may improve the radiomic model performance on the pilot dataset. The motivation behind reproducing the study by Mattonen et al. is because it is the first-ever study published in the application for early detection of local recurrence in lung SABR patients with excellent classification performance. The aim of the principal study is to extend the radiomics study into a larger pool of patients and to evaluate the sets of radiomic features on an independent validation set.89 The main objective of the principal study is to improve the radiomic model performance even further. In the next chapter, a general workflow of radiomics is described. Chapter 3 and 4 describes the radiomics workflow implemented in the current application to distinguish a local recurrence from fibrosis. 16 Chapter 2: Theory of Radiomics This chapter describes an overview and each component of a radiomics workflow. 2.1 Introduction Medical Imaging is a crucial part of cancer therapeutics and it is used extensively in clinical decision making all around the world. Nevertheless, with the advent of scientific studies such as radiomics, the role of medical imaging is rapidly developing from mainly a diagnostic tool to into an important part of personalized precision medicine.90 Medical images contain a vast amount of information that cannot be simply visualized by a human observer. Currently, qualitative features are commonly used in radiology and oncology to describe a tissue of interest. Quantitative features extracted with software using mathematical algorithms, could provide extensive information about a tissue based on its shape, gray level intensity and spatial arrangement of those intensity values.85 This quantitative information, known as the higher dimensional data can be further analyzed using a radiomics model. In 2012, Philippe Lambin presented the concept of radiomics, which was defined as âthe extraction of large number of quantitative image features from diagnostic images using a high-throughput approachâ.91 Rapid increase in research on radiomics soon followed and the definition was updated in 2014 to âthe automated or semi-automated extraction of a large number of quantitative image features from a volume of interest in the radiographic or diagnostic medical imagesâ.86 These radiomic features are simply quantitative image features which are calculated using advanced texture and shape analysis.87 The qualitative description of texture features (smoothness, roughness, coarseness etc.) needs to be quantified for the digital image data.87 Quantitative description of the features reveal different levels of complexity and provides information about the properties of the tissue of interest.85 The domain of texture analysis, which is the foundation of radiomics was developed by Claude Shannon and further extended by Haralick, Galloway and others in 1948, enabling the calculation of various statistical measures to integrate gray level matrix operations on the image data.92â95 Thus, a radiomics study is based on incorporating advanced texture analysis, shape and gray level statistics within the medical images to discern their relationships to a certain clinicopathological data or clinical outcomes.87 Models built upon the relationship between radiomic features and clinical outcomes have the potential to enhance clinical decision support systems. This chapter describes a general radiomics workflow. One of the main limitations of radiomics is that the features are known to be highly dependent on different CT image acquisition parameters. A recent study showed that various technical parameter 17 settings (i.e. reconstruction kernel, mAs etc) tested on one CT scanner model influences the values of radiomic features suggesting intra-scanner variability.96 Other recent publications have also revealed that image acquisition parameters across scanners affect the radiomic feature values. For instance, several studies specifically examined the impact of slice thickness and pixel size on radiomic features.97â100 Slice thickness may be adjusted depending on the patient to optimize image quality and radiation dose.101 Unfortunately, radiomic features are also affected by the way in which image pre-processing and reconstruction is performed.90 Different techniques in pre-processing (such as filtering) and reconstruction (such as filtered back projection or iterative reconstruction) can affect the radiomic features.90 Therefore, to conduct a robust multi-center radiomics study with numerous CT scanners and image acquisition protocols, harmonization of CT image or radiomic features is crucial.90,102 Despite these limitations, there are several important advantages of radiomics over current clinical evaluation. First, to conduct a radiomics study, there is no additional requirement in terms of the input data. The medical imaging data used in day-to-day clinical diagnosis can be suitable for radiomic analysis (the focus is primarily on CT in this study). Secondly, the results from radiomics studies may provide useful information about the entire tumour, whereas a conventional clinical workflow involves biopsy intervention, which gives information of only a subset of a tumour at a certain time point.86 Radiomics is a relatively simple and non-invasive tool that enables extraction of the whole tumour information at any timepoint and also of any subregions within the tumour.86 Obtaining information about the entire tumour rather than a small subset may be valuable as tumours are often heterogenous.103 In a few studies it was reported that tumours with more genomic heterogeneity are less sensitive to the cancer treatment.104,105 Therefore, obtaining heterogeneity information can be beneficial in tumour management.86 Lastly, radiomics can potentially prevent invasive medical interventions and procedures that might expose the patients to unnecessary risks with no additional benefit. The comparison between conventional clinical and radiomics workflow is illustrated in figure 4. Radiomics model building is further illustrated in figure 5. The radiomics workflow can be broadly divided into five phases: data selection, medical imaging (involving image acquisition and segmentation), feature extraction, analysis and modelling. Figure 5. summarizes the radiomics workflow. 18 Figure 4. Illustrates the conventional model vs radiomics model. (Adapted from Morin et al.).84 2.2 Data selection A radiomics workflow begins with selecting the data. The data selection part includes choosing the patients, structure/region of interests, imaging protocols and clinical outcomes to be predicted. Generally, the structure of interest is the tumour and the features extracted from the tumour is then combined with data of the clinical outcome of the treatment, for instance survival rate.90 The data may include subregions of the tumour, metastases and also normal tissues. In this study, the predicted clinical outcome or the event of interest is a recurrence. Other criteria of data selection may include carefully choosing histology, treatment methods and imaging modality (CT, PET and MRI etc). As mentioned before, recent studies have highlighted that many radiomic features lack reproducibility, meaning that the feature values are highly influenced by different scanners, image acquisition and reconstruction parameters.97,106,107 The lack of reproducibility of radiomic features makes numerous radiomics studies frequently difficult to interpret and compare among each other. Therefore, in some studies, the data selection part may include extracting CT images scanned with only similar imaging acquisition parameters. 19 Figure 5. Radiomics workflow (Adapted from Aerts et al.).102 2.3 Medical Imaging 2.3.1 Segmentation Segmentation is required in radiomics. Manual segmentation by a radiologist is regarded as the âgold standardâ, however it is time consuming and not advised to be used in a large dataset. Additionally, manual segmentations are subject to inter- and intra-observer variability.108 An ideal segmentation tool must have the following characteristics: automation, reproducibility and accuracy. Varying segmentation (meaning varying voxels within the image) can introduce bias in the analysis of extracted radiomic features, therefore it is very important to reduce this type of bias. Some methods that reduces this variability are performing multiple segmentations and evaluating them by different clinicians or simply exploring other segmentation algorithms. A fully automated segmentation may be attractive, quick and reproducible, but lack of user input may raise questions of reliability. Semi-automated segmentation methods on the other hand require minor user interactions, but the final contour is segmented based on the predetermined input parameters. Semi-automated segmentation algorithms are widely accepted because they are quick, reproducible and require minimal user input or minimal modifications. Different segmentation algorithms and supporting software are readily available.109, 110 3D Slicer is an open source-software platform that includes a variety of segmentation tools.111 GrowCut segmentation, a type of semi-automated segmentation algorithm available in 3D Slicer, has recently been reported to efficiently reduce inter-observer variability.86 Before the automated segmentation, a set of labeled pixels must be entered by 20 the user.86 The algorithm has a binary labelling system; object and background. The object in this case is the label inside a region of interest (ROI) and the background label is a margin that surrounds the region outside the object. The algorithm then automatically generates the segmentation for the ROI based on the binary labels.86,112 2.3.2 Imaging at multiple points Another source of inconsistency in radiomic features can be the change in organ volume and motion over a certain time, for example respiratory motion in lungs. Although identical image acquisition parameters and CT scanners are used, different anatomic, physiologic and positional variations of the patient can influence the radiomic feature values as well.90 A technique called test-retest can be used in this case to reduce the variability (such as scanning two images within a short period of time on a single patient). Radiomic features that are stable across the two consecutive scans may be used for modelling. However, this type of test-retest on a patient often isnât practical in a clinical setting due to the increased dose to the patient. Datasets specific for test-retest study are available online.113 However, the test-retest analysis was not performed due to a lack of time. 2.4 Feature extraction 2.4.1 Standardization of radiomics pipeline Radiomics typically uses a high-throughput technique to extract quantitative radiomic features that characterizes âthe region of interestsâ. In addition to the image acquisition parameters, there is a need to have a standardization in the extraction of features, the feature nomenclature, the mathematical definition, and the implementation in the software.90 Many radiomics studies have used in-house software that was not shared with the public, making it difficult to reproduce the study. Pyradiomics is an open-source software package that was developed to address this challenge and provide a standardization of the feature definitions.114 Furthermore, most features in the Pyradiomics toolkit are calculated based on the guidelines recommended by the Imaging Biomarker Standardization Initiative (IBSI) to enable better comparisons among different radiomics studies.114,115 2.4.2 Texture feature and analysis in Radiomics Texture analysis is a core and fundamental concept in radiomics. It refers to wide variety of quantitative methods that are used to assess the gray-level intensities and pixel positions within the image in order to derive texture features.116 As mentioned above, texture features are helpful in providing information about the tumour heterogeneity, information which is not readily apparent with our visual capabilities. There are 21 different methods used to describe and formulate those features; statistical-, model- and transform-based techniques.116 Statistical-based techniques are the most commonly used method for texture analysis, which comprises of the three following measuring parameters: First-order statistics describes the frequency distribution of voxels or gray-level intensities within a region of interest. This information can be obtained from the histogram of gray-level intensity values.117 First-order statistics or features do not offer any information about the spatial relationships with other pixels or voxels, but are dependent on pixel/voxel values. First-order statistics typically includes features such as mean, maximum, minimum, uniformity, entropy, skewness and kurtosis. Second-order statistics are a subset of texture features, which describes statistical interrelationship between voxels. Second-order features include features calculated through run-length, co-occurrence matrix measurements which evaluate lengths of voxels or pixels that have equal gray-level intensities in a consecutive manner.118 These features determine how a pixel intensity i is located itself within a certain spatial relationship to pixel intensity j. Second-order features calculated based on a gray-level co-occurrence matrix (GLCM) include entropy, energy, homogeneity and correlation. The other technique to derive features is based on gray-level run-length matrix (GLRM), which evaluates texture in a specified direction. The difference between GLCM and GLRLM is that, GLCM matrix calculates the spatial relationship between two pixels at the time in the image (central and the neighboring pixel), whereas GLRLM considers runs of pixel with identical gray level values. A run is defined as length of consecutive pixels in a specific direction. The relationships between different run lengths provide information about the texture. For instance, fine texture is associated with short runs with similar pixel intensities whereas coarse texture is associated with more long runs with different intensities.118 Some of the features in GLRM may include gray-level non-uniformity, short-run emphasis, run length non-uniformity etc. Gray level dependence matrix (GLDM) is defined as a number of connected voxels dependent on a center voxel with a certain distance δ. GLDM features may include features such as Dependence non-uniformity and Small dependence emphasis.114 High-order statistics are another subset of texture feature calculated based on the neighborhood grey-tone difference matrix (NGTDM), which evaluates the spatial interrelationships between 3 or more pixels. These features provide a complete information of the tumour; therefore, it is believed to match and resemble the visual experience of a human. The features may include contrast, coarseness and busyness etc.116,118 Gray level size zone matrix (GLSZM) is subset of high-order statistics and is defined as number of connected voxels that have equal gray level intensity. This include features such as short zone 22 emphasis, size zone variability and short run emphasis. Appendix A provides an example calculation for each feature classes (GLCM, GLSZM, GLRLM, NGTDM, GLDM). 114 Shape based features may also be calculated in a radiomics study. They are not dependent on the gray level values, but are extracted directly from the segmentation of the region of interest. It essentially describes the geometric shape descriptions of 3D or 2D image. In summary the radiomic features calculated in this study using the pyradiomics library are subdivided into the following classes: 114 1. First order statistics 2. Shape-based features 3. Gray Level Cooccurrence Matrix (texture, second order) 4. Gray Level Run Length Matrix (texture, second order) 5. Gray Level Size Zone Matrix (texture, higher order) 6. Neighbouring Gray Tone Difference Matrix (texture, higher order) 7. Gray Level Dependence Matrix (texture, second order) 2.5 Feature harmonization Feature harmonization between different scanners and image acquisition parameters is an emerging area of research, but there is no standard method for radiomic feature harmonization at this time. A batch-effect correction method called ComBat was used in a few recent radiomics studies.119,120 A recent study from Maastricht University involves feature harmonization using phantom studies (Gammex 467 CT Phantom) that may provide accurate models to correct the radiomic features based on signal-to-noise ratio (SNR) dependence.121 Phantom studies can be especially useful in determining radiomic features that rely on the vendor and specific scanner machines.90 Applying a Butterworth low-pass filter in the frequency domain for every CT image has been reported to be an effective method to reduce feature variability due to differences in the pixel size.101 A simple, yet effective method is proposed in this study where feature normalization using mean and standard deviation is performed individually across the different CT scanner manufacturers. The motivation behind this proposed method is to project all features extracted from the different scanner vendors onto the same distribution, based on its respective center mean and standard deviation. 23 2.6 Analysis 2.6.1 Feature selection It is important to note that not all the radiomic features are useful in predicting a particular outcome of an event. Furthermore, incorporating redundant features will reduce computational and analytical power, which is why feature selection is crucial in the radiomics workflow. Incorporating redundant features may lead to overfitting of the model. Overfitting a model reflect the noise in the image rather than the underlying original data.87 Therefore, removing redundant features may prevent overfitting. Feature selection is also essential in preventing overfitting of the model to the training data.90 There have been various methods implemented in analysis, such as principal component analyses (PCA), which is a dimensional reduction technique used to remove redundant features to prevent overfitting the model.90 Some analyses use clustering where highly correlated radiomic features are grouped together. Those features that do not necessarily provide additional information are removed from the final set of features. Therefore, feature selection is an essential part of the radiomic analysis.90 Generally, feature selection can include techniques based on supervised and unsupervised feature selection. Supervised feature selection can be performed using a filter or a wrapper approach. A subtype of wrapper method, called the forward feature selection, works in such a way that a feature is only added in an empty set if the addition of the new feature to an existing subset can improve the model performance. If there is no improvement in the model, the feature is not added. 1-nearest neighbour was used as the underlying model for the forward feature selection algorithm, because it provides a good approximate of the expected generalization error and furthermore, it is computationally efficient.122 Forward feature selection is illustrated in figure 6. Figure 6. Forward Feature Selection 2.6.2 Modelling and classification The final phase of radiomics workflow is modelling. Modelling in itself can involve three main aspects: feature selection (in the previous section), modelling methods (machine learning algorithms) and 24 validation. This part of the radiomics process must be done carefully so that overfitting the data does not negatively affect the model performances. This is done so by separating the entire dataset into a training and an unbiased test set. Modelling methodology must be optimal and thus testing multiple machine learning algorithms can be an advantage. Finally, the model must be both internally and externally validated to complete the radiomics workflow. Machine learning is a technique which falls under the field of artificial intelligence. In machine learning, an algorithm learns by inference from a set of data points.123 Machine learning algorithms can be used for classification purposes where the purpose of a classifier is to predict a class of given datapoints. There are mainly two ways in which one can perform this part of the analysis; Supervised and Unsupervised classification.87 In supervised classification, the user must provide input for patients whose underlying disease or pathology is known whereas unsupervised classification builds the model based on patients with unknown pathology. Thus, there are no labels in the data required for unsupervised learning. Unsupervised classifier simply categorizes the patients into groups based on some form of distance metric (i.e., angular distance, Euclidean distance).87 One of the most widely used supervised classification algorithms is the Support Vector Machine (SVM), which was introduced in 1992 by Boser, Guyon and Vapnik.124 The SVM works in such a way that the algorithm automatically tunes the capacity of the classification function by maximizing the boundary between the training dataset and class boundary.124 The resulting classification relies on the data that are closest to the decision boundary, which are known as the supporting patterns or support vectors. The decision boundary is called a hyperplane. In the training phase, the algorithm constructs a separating hyperplane with a maximum margin based on the training data that will be used to validate the test set. The SVM is illustrated in figure 7. Figure 7. Support Vector Machine. 25 The last part of modelling process is validation. With machine learning, it is important to have a separate training and test set. An unbiased test set is used to validate the generalizability of the model and ensure that it is not overfitted to the training set. It is essential to have an independent validation set that can provide information about the true performance of a model for an unknown dataset. The validation includes model performance, which can be measured in terms of discrimination and calibration. Discrimination is usually determined using the receiver operating characteristics (ROC) curve, or the area under the ROC curve (AUC).90 The AUC of the ROC curve describes the sensitivity and specificity of the model, which denotes the probability of a randomly selected patient to have the predicted clinical outcomes that matches the true underlying clinical outcomes.90 The sensitivity, or the true positive rate, of the model is the ability of a test to correctly classify a patient as âdiseasedâ.125 The specificity or the true negative rate on the other hand is the ability to accurately classify a patient as âdisease-freeâ.125 True positive rate and True negative rate can be calculated using the following equations ðððð ðð¡ðð£ðð¡ð¦ = ððð¢ð ððð ðð¡ðð£ð ð ðð¡ð = ððð¢ð ððð ðð¡ðð£ðððð¢ð ððð ðð¡ðð£ð + ð¹ððð ð ððððð¡ðð£ð ðððððððððð¡ð¦ = ððð¢ð ððððð¡ðð£ð ð ðð¡ð = ððð¢ð ððððð¡ðð£ðððð¢ð ððððð¡ðð£ð + ð¹ððð ð ððð ðð¡ðð£ð 26 Chapter 3: Pilot Study: Evaluation of previously reported radiomic features in a new patient dataset This chapter describes the pilot project, which aims to reproduce the previous research performed in Western University (London, Ontario) on early detection of lung cancer recurrence. In the pilot study, we used radiomic features proposed in the Mattonen et al. study and evaluated those features in our patient dataset. 3.1 Introduction The established treatment for the medically inoperable early-stage NSCLC patients, or patients who refuse surgery, is Stereotactic ablative radiation therapy (SABR)47. One limitation of SABR is that the treated patients often develop a radiation-induced lung injury called fibrosis, which can be similar to a local recurrence in appearance.89 While a local recurrence is not common in the patients treated with SABR (expected at about ~ 10% at 5 years), accurate, early detection is essential to managing disease progression.47 A local recurrence is not generally observed until around 15 months after the treatment and may manifest anywhere up to 5 years or later.65 False positives can lead to additional unnecessary imaging and also invasive medical testing. False negatives may lead to a delay in salvage therapy. Salvage therapies are additional treatments that may be available to patients who do not respond to the initial treatment..26 In some cases, patients had surgical resection, which ultimately proved to contain only a benign radiation-induced injury exposing the patients to unnecessary risks.89 In summary, detection of local recurrence accurately and as early as possible is essential in order to allow a timely salvage treatment when appropriate or, alternatively, reduce risks of unnecessary medical interventions for a benign radiation injury.89 Previous research by Sarah Mattonen et al. has shown the capability of radiomics in predicting local recurrences earlier than 6 months.89 Their study used CT follow-up images of less than 6 months from 45 patients (15 recurrence and 30 non-recurrence) treated with SABR. Using a radiomics-based model, a false positive rate of 24% (TNR = 76%) and false negative rate of 23.1% (TPR = 76.9%) were achieved using a leave-one-out cross validation approach. This pilot study aims to reproduce the previous research done by Sarah Mattonen et al. at the University of Western Ontario using a different patient dataset.89 The aim of this pilot study is to evaluate the previously found radiomic features in a different dataset and potentially extend the study by implementing a similar feature selection approach to define a new set of radiomic features that may improve the performance of the radiomics model using the earliest follow up CT images. 27 3.2 Material and Methods 3.2.1 Creating a database with follow-up images for lung SABR patients The medical imaging reports of lung cancer patients treated with SABR were reviewed to identify patients with a local recurrence. The date range of CT scans performed between 1st Jan 2011 to 1st Feb 2019 were included in the study according to the Research Ethics Board approval. In total, the medical reports of 500 patients were reviewed. The list of selected patients who supposedly had local recurrence was further reviewed by an experienced radiation oncologist for verification, and a final list of patients with a confirmed local recurrence was created. The radiation oncologist used a combination of validated high-risk CT features (primarily), PET imaging reports and interventional biopsy procedure reports to finalize the list of patients with local recurrence. CT images of the selected patients were extracted into a local BC Cancer Kelowna server with an anonymized unique ID (detail steps of anonymizing in the later sections). For non-recurrence data, patient reports which had the keyword âno recurrenceâ and or âfibrosisâ, follow-up CT images were extracted and anonymized in the same location. Aside from the recurrence and non-recurrence information, the database included information extracted from the patient medical reports such as the anonymized ID, treatment start date, treatment dose, treatment modality, sex, date of birth, date of death (if applicable), laterality, tumour location, methods of recurrence detection, lung cancer histology and date of local recurrence detection. In total, 79 patients were selected who had either confirmed recurrence or non-recurrence, out of which 28 patients were selected who were scanned with contrast-enhanced CT imaging protocol to enable a direct comparison to the previous study. However, patients scanned with only lung reconstruction kernel were used in the pilot study since majority of the patients were scanned using a lung kernel. CT images scanned with B60f reconstruction kernel was used in the Mattonen et al. study. Both the CT images and lung SABR treatment plans were anonymized in MIM (MIM Software Inc., Cleveland, OH). Private patient information (name, patient ID, date of birth etc.) on the CT image and treatment plans were cleared and anonymized before exporting the CT scans to the local BC cancer drive. 3.2.2 Patient selection for pilot study The pilot study dataset consisted of patients with Adenocarcinoma, Squamous cell carcinoma, Metastasis and unspecified NSCLC. The proportion of patients with aforementioned histology were shown in table 2. The dataset contains 10 patients with a local recurrence and 18 with a non-recurrence. Of the 10 patients with confirmed local recurrences, 4 were confirmed with PET imaging and 3 were confirmed with CT scans (high-risk radiologic features) and rest of them were not specified (mode of recurrence 28 detection information not available). In the whole dataset, 13 patients were treated with 3D Conformal Radiation Therapy (3DCRT) and 15 with Volumetrically Modulated Arc Therapy (VMAT) technique. Both 3DCRT and VMAT techniques conform the radiation dose to the tumour sparing healthy tissues (16 patients received 4 fractions of dose; 12 patients received 8 fractions of dose). All the patients in this dataset were imaged with a GE LightSpeed VCT or GE Discovery CT 750 HD scanner (GE Healthcare, Madison, WI). All the follow-up CT images were reconstructed with a lung reconstruction kernel, and only contrast-enhanced images were used in this pilot study to obtain a direct comparison between our study and the previously published study from Western University.89 Table 2. The baseline characteristics of the patients chosen in the pilot study. Characteristic All Patients (n = 28) Recurrence (n = 10) NonâRecurrence (n=18) Age median (range) 74 (54 â 86) 72.5 (54 â 78) 75.5 (66 â 86) Male â N (%) 14 (50) 3 (30) 11 (61) Involved lobe â N (%) Left Upper Lobe 4 (14) 2 (20) 2 (11) Left Lower Lobe 6 (21) 1 (10) 5 (28) Right Upper Lobe 9 (32) 3 (30) 6 (33) Right Middle Lobe 1 (4) 0 (0) 1 (6) Right Lower Lobe 8 (29) 4 (40) 4 (22) Tumour types â N (%) Adenocarcinoma 8 (29) 4 (40) 4 (22) Squamous cell carcinoma 2 (7) 1 (10) 1 (6) NSCLC/Not specified 14 (50) 3 (30) 11 (61) Metastasis 4 (14) 2 (20) 2 (11) RT technique 3DCRT 13 (46) 7 (70) 6 (33) VMAT 15 (54) 3 (30) 12 (67) Fractionation â N (%) 4 16 (57) 6 (60) 10 (56) 8 12 (43) 4 (40) 8 (44) Recurrence mode â N 29 (%) CT alone 8 (29) 3 (30) 5 (28) PET 7 (25) 4 (40) 3 (17) Not specified 13 (46) 3 (30) 10 (55) 3.2.3 GrowCut Segmentation The segmentation of the tumour (local recurrence) and fibrosis (or non-recurrence) was done in an open source software called 3D Slicer.111 A semi-automated segmentation algorithm called GrowCut was used throughout the pilot study. GrowCut segmentation requires the user to select the foreground and background pixels. The region of interest is roughly segmented by placing seed pixels within the tumour or fibrosis and the margin that surrounds it. The algorithm performs automated segmentation based on the seed pixels and the margin using cellular automaton. This segmentation algorithm is iterative, as cellular automaton labels the surrounding pixels as either inside the region of interest or not, the user can guide the segmentation algorithm to modify the initial input seed to alter the final segmentation outcome.112 The GrowCut segmentation is illustrated in figure 8. After the output of GrowCut segmentation is carefully analyzed, manual modifications were made in accordance with the contoured region in the radiotherapy treatment plan. 3D slicer (version 4.11) has GrowCut algorithm implemented as âGrow from seedsâ tool that can be found in segment editor module. This particular workflow was implemented for all the patients in the study. The modifications of the final segmentation were performed according to the location of the tumour in the anonymized treatment plan. Both the input CT image and its corresponding segmentation were stored as Centername â uniquerollnumber.nrrd and Centername â uniquerollnumber.seg.nrrd for feature extraction. After the conversion to nrrd (nearly raw raster data: a file format for the representation and processing of n-dimensional raster data), a batch processing script was implemented to extract radiomic features for all 28 patients in one csv file. 30 Figure 8. GrowCut segmentation algorithm available in the 3D Slicer software. The GrowCut algorithm requires two input; approximate region of interest (green) and boundary (yellow). The final segmentation is produced using a cellular automaton algorithm. 3.2.4 Feature extraction using pyradiomics All the radiomics feature extraction was done in an open source python-based package called pyradiomics.114 The settings used for pyradiomics are shown in Appendix B. Briefly, all the CT images were first interpolated to 1mm Â´ 1mm Â´ 2.5mm, using Sitkspline function and normalized to an intensity scale of 0 - 1. The default 108 radiomic features were extracted using a batch processing python script (pyradiomics github).114 The 108 radiomic features that were extracted can be classified into seven feature classes; first order, shape, Gray Level Dependence Matrix (GLDM), Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRM), Gray Level Size Zone Matrix (GLSZM) and Neighboring Gray Tone Difference Matrix (NGTDM). 3.2.5 Analysis using radiomic features from previous research The top five radiomic features for early detection of a local recurrence that were found by Mattonen et al. were minimum grey-level, GLCM homogeneity, GLCM correlation, GLCM energy and grey-level uniformity (details in table 7 in Appendix C).89 These features were evaluated using Support Vector Machine with the same setting used in the previous research, however the implementation was performed in R.126,127 3.2.6 Feature Selection After evaluating the top five radiomic features found in the research by Mattonen et al., a similar feature selection approach was performed for our patient dataset to determine the optimal set of radiomic features in order to improve the performance of the radiomics model (false positive and false negative rates). Leave-one-out cross-validation approach was used in feature selection (see figure 9). At each cross-validation fold, a stepwise forward feature selection with 1-nearest neighbor classifier was used to select 31 the topmost frequent features. N (number of patients) times 10 features were found at each fold and the top five most frequently voted features amongst the N*10 matrix were selected for classification (see figure 10). The feature selection part was completed in python. Figure 9. The schematic diagram of leave-one-out cross validation approach. The top five features selected using this method were Size Zone Non-Uniformity Normalized (GLSZM), Inverse Difference Moment Normalized (GLCM), Small Dependence Low Gray Level Emphasis (GLDM), Small Area Low Gray Level Emphasis (GLSZM). Figure 10. Counting radiomic features based on leave-one-out cross validation, at each fold all the features are counted. 32 3.2.7 Classification using Support Vector Machine A support vector machine classification (with nu algorithm; linear kernel) was used to classify the local recurrences from non-recurrences. Nu is the regularization parameter in the support vector machine learning algorithm. Linear kernel is used when the dataset is linearly separable and one advantage of using a linear kernel is that it is computationally fast. The gamma and cost setting were set to 0.5 and 4 respectively. The gamma and cost parameter for the support vector machine were selected using a grid search parameter tuning algorithm.126,127 The top five features found in the previous section were internally cross validated (with 3, 5, 10 and leave-one-out folds) in the 5-9 months follow up CT scans using the support vector machine classifier. The results in pilot study include the classification done using the radiomics features i) from the previous study and ii) which were optimized for our patient data set. All the radiomics modelling and analysis was done in R language. 126 3.3 Results The first section of analysis was done on the current dataset using the top five features found in the previous study. The top five radiomic features reported by Mattonen et al. is minimum gray-level, GLCM homogeneity, GLCM correlation, GLCM energy and gray-level uniformity. Table 7 in Appendix C shows the top five features in the previous study and the corresponding five features that were calculated in the pyradiomics library GLCM homogeneity feature calculated in the previous study has been deprecated in the pyradiomics package; however, the pyradiomics package recommended a mathematically equivalent feature called Inverse Difference (ID) that measures the local homogeneity. GLCM Energy in previous study has been named GLCM Joint energy in pyradiomics package, however the formula for feature calculation is identical. The calculation and name of the other three features were the same in the pyradiomics library as in the previous study. Leave-one-out, 10 folds, 5 folds and 3 folds cross validation were performed using support vector machine classifier (settings in the previous section) on the 28 patients. A k â fold process is defined when the dataset is divided into k unique groups. In each loop, a test group is held out and remaining samples are used for training the model. Figure 11 summarizes the results of leave-one-out, 10 folds, 5 folds and 3 folds cross validation performances. 33 Figure 12. Illustrates the radiomic performance of the new features found using the feature selection method explained in this chapter. Figure 11. Illustrates the radiomic performance of the features found in previous research (Mattonen et al.), an internal cross validation using 3-folds, 5-folds, 10-folds, leave-one-out was performed. The top five radiomics features that were found using a similar feature selection approach performed in the previous study were Size Zone Non-Uniformity Normalized (GLSZM), Inverse Difference Moment Normalized (GLCM), Small Dependence Low Gray Level Emphasis (GLDM), Small Area Low Gray Level Emphasis (GLSZM), Coarseness (NGTDM) shown in table 8 in Appendix D. To evaluate the predictive performance of the top five features, leave-one-out, 10 folds, 5 folds and 3 folds cross validations were done on the entire dataset. Figure 12 summarizes the performance of the radiomic features. The top five features selected using the current dataset improves the performance over the sets of features selected by the previous study by Mattonen et al. For leave-one-out cross validation, the true positive rate improved by a factor of two. True negative rate has also improved significantly (figure 12). 34 3.4 Discussion In this pilot study, five radiomic features found in the previous study (predictive of a local recurrence) were validated and furthermore a similar feature selection was performed using a python-based script to propose a new set of radiomics features that may improve the radiomics performance. The set of radiomic features found in the previous study did not produce acceptable results in our patient dataset with a poor classification performance. In leave-one-out cross validation, false negative rate (FNR) and false positive rate (FPR) of 23.1% and 24% were obtained in the previous research whereas in this study, FNR and FPR of 80% and 72% were found using the same radiomics features. While using a new set of radiomics features, the performance was improved by almost a factor of two in the case of leave-one-out cross validated result. The FNR and FPR of 60% and 55% were obtained. One of the main differences between the pilot study and the Mattonen et al. study is data selection. CT images scanned with General Electric scanner was used in the pilot study (due to data availability) and Phillips and Siemens scanned CT images were used in the Mattonen et al. study. This may have caused the discrepancy in the results between the two studies. Additionally, although the formula of the radiomics feature were similar, their implementation did vary. For instance, the GLCM calculation in the previous study was calculated within four in-plane neighboring voxel directions [(-1,1,0), (-1,0,0), (0,1,0), (-1,-1,0)], and the final texture features were averaged over all directions (see Appendix E). Herein pyradiomics package, a symmetrical GLCM option was chosen for texture feature calculation. This definition of the GLCM radiomics features may not produce consistent results with the previous research and needs to be further investigated. It should also be noted that, a preprocessing step was included using the pyradiomics customization setting. All the CT images were first interpolated using a SitkSpline function to a 1 Â´ 1 Â´ 2.5 mm voxel size and gray level normalization was performed to the range 0-1. Gray level normalization and voxel interpolation was performed as the initial step towards radiomics feature harmonization.128 A study found that gray level normalization and voxel interpolation reduced the radiomics feature dependency on varying voxel sizes, which are present commonly in multi-center study.128 In other words, the radiomics feature extracted after voxel interpolation and gray level normalization are more stable. The previous study by Mattonen et al. did not report any gray level normalization or voxel interpolation in their radiomics workflow, the feature extraction was done on the raw CT images without any preprocessing. A limitation of the comparison of the pilot study to the previous research is the approach towards segmentation and feature extraction. The top four features in the previous study were found in a peri-35 consolidative region and one feature from consolidative region essentially separating the segmentation into two regions. In our study, mainly the consolidative region was used as the segmentation to select radiomic features and evaluate the model because performing two segmentation and feature extractions on one patient generates more complex radiomics feature selection process. In future studies, two separate segmentations for one single patient may be performed by separating the region into peri-consolidative and consolidative region. The other limitation of the study is that the radiomic feature performances were investigated based on a cross validation approach. The ideal way in which a radiomic study could be conducted would be to separate training and validation set, which may reveal the unbiased performance of the features. 3.5 Conclusion The radiomic features that were previously reported to be predictive of local recurrence were evaluated in the current study. The results obtained in current study showed lack of generalizability of the radiomic feature signatures found previously. Nevertheless, using the same feature selection approach, an improvement in radiomics model performance was observed. The pilot study showed that there is a need to perform radiomics modeling on larger pool of patient dataset and therefore validate the radiomic features on an entirely independent dataset rather than performing internal cross validation. This motivation led to the extension of the pilot study, which will be explained in the next chapter. 36 Chapter 4: Principal study: Radiomics study incorporating a harmonization process to include CT images with diverse image acquisition parameters This chapter describes the principal study, which is the extension of the pilot project. The purpose of the principal study is to harmonize the radiomic features extracted from CT scanners throughout the province, to include diverse scanner vendors and imaging protocol into the study. 4.1 Introduction The pilot study in the previous chapter attempted to reproduce the research done by Mattonen et al. that found five radiomic features that may potentially detect a local recurrence in post-SABR within early follow up period.89 One major limitation of the pilot study is that there are several important distinction between the radiomics workflow of the two studies, therefore a complete reproducibility may not have been achieved (detail explanation in chapter 3). Firstly, the set of radiomic features proposed by Mattonen et al. was validated. Secondly, using their feature selection method, five new radiomic features were found and evaluated. The comparisons between the performances of the sets of radiomic features were done based on the results from internal cross validations due to the dataset being small. The pilot study indicated that the performances of the radiomic features reported in their study may not be consistent in a new dataset. Furthermore, the pilot study was conducted only on a subset of entire dataset of patients who were scanned with contrast-enhanced CT scans with lung reconstruction kernel. Therefore, due to the limited number of patients in the pilot study, splitting the data into a separate training and test set was not feasible. An unbiased test set is ultimately required in radiomics to evaluate the performance of the model. This chapter describes the principal component of the thesis, which builds upon the pilot study (chapter 3). The purpose of the principal study is to perform radiomics on a larger and more diverse patient dataset (i.e. with various image acquisition parameters) in order to build a model that is more generalizable and robust in differentiating a local recurrence from fibrosis. The principal study is more relevant and applicable towards the clinical decision-support system (CDSS) because CT images from a variety of protocols and scanners resemble a realistic scenario of the clinical workflow in a distributed healthcare system. Furthermore, incorporating a greater number of CT images in the study improves the statistical power of the radiomics model. Previous studies demonstrated the need to assess the predictive capability of the radiomic features on an independent validation set to determine if the features are predictive of a local recurrence and fibrosis. The lack of statistical power (i.e. number of patients in the entire dataset) in the previous studies has 37 driven the principal study to propose a new method of feature harmonization across various CT scanner manufacturers and reconstruction algorithms. Consequently, more images from a variety of scanners through the province can be used to build and validate the model. In this study, a simple yet effective feature harmonization method was proposed. The hypothesis of the principal study is that by incorporating a simple feature harmonization technique, radiomic features will be able to accurately distinguish a local recurrence from fibrosis, the results of the model performance will be validated with an unbiased test set. The majority of the workflow including feature extraction and selection were identical to the pilot project. The key difference is that three independent validation sets were used to investigate if the radiomic features selected are generalizable and, finally, determine the performance of the radiomics model. 4.2 Material and Methods The material and methods in the principal study are closely related to the pilot study in chapter 3. More patients are included in the principal study since various scanners and image acquisition parameters are included in the study. To enable this, the radiomics workflow requires an additional step of feature harmonization after the feature extraction. Furthermore, as mentioned before, three independent validation sets were reserved to validate the performance of the radiomics model. 4.2.1 Baseline patient characteristics As described in the pilot study, the medical imaging reports of lung cancer patients treated with SABR were reviewed to identify patients with a local recurrence and fibrosis. Out of 500 patients reviewed (date range from 1st Jan 2011 to 1st Feb 2019 according to REB application), the list of selected 79 patients who supposedly had confirmation of a local recurrence (26 patients), as well as patients with no recurrence (53 patients) were selected using their medical imaging reports. The final patient dataset (n=79) consists of patients with Adenocarcinoma, Squamous cell carcinoma, Metastasis and unspecified NSCLC. Table 3 summarizes the baseline patient characteristics of the patients included in the principal study. The final list of patients with local recurrences were confirmed by an experienced radiation oncologist using the high-risk CT features, PET imaging and interventional biopsy reports. Patients with a non-recurrence were selected if the medical imaging reports contained the keyword such as âno recurrenceâ and/or âfibrosisâ. Of the 26 patients with confirmed local recurrences, 8 were confirmed with PET imaging, 11 were confirmed with follow-up CT imaging and the remaining did not have a method of detection explicitly specified. The routine follow-up CT scans used were from 5-9 months, 9-12 months and 24 months (median follow up) for all 79 patients. 38 Table 3. Baseline patient characteristics of patients (n=79) used in the principal study. Characteristic All Patients (n = 79) Recurrence (n = 26) NonâRecurrence (n = 53) Age median (range) 71 (54 - 88) 67 (52 â 88) 76 (61 â 86) Male â N (%) 46 (58) 13 (50) 33 (60) Involved lobe â N (%) Left Upper Lobe 21 (27) 10 (38) 11 (21) Left Lower Lobe 15 (19) 5 (19) 10 (19) Right Upper Lobe 25 (32) 3 (12) 22 (41) Right Middle Lobe 5 (6) 2 (8) 3 (6) Right Lower Lobe 13 (16) 6 (23) 7 (13) Tumour types â N (%) Adenocarcinoma 26 (33) 9 (35) 17 (32) Squamous cell carcinoma 13 (16) 3 (12) 10 (19) NSCLC (not specified) 21 (27) 7 (27) 14 (26) Metastasis 13 (16) 4 (15) 9 (17) No biopsy 6 (8) 3 (12) 3 (6) RT technique 3DCRT 26 (33) 8 (31) 18 (34) VMAT 47 (59) 14 (54) 32 (62) Not specified 6 (8) 4 (15) 2 (4) Fractionation â N (%) 4 44 (56) 12 (46) 32 (60) 8 35 (44) 14 (54) 21 (40) Recurrence mode â N (%) CT alone 24 (30) 11 (42) 13 (25) PET 15 (19) 8 (31) 7 (13) Not specified 40 (51) 7 (27) 33 (62) 39 In this study, patients from four BC cancer centers (Kelowna, Vancouver, Victoria and Surrey) were included. Furthermore, patients from those centers may have CT scans acquired in regional hospitals throughout the province, with different imaging protocols, generating a largely unharmonized dataset. Table 4 shows the breakdown of scanner vendors, model and reconstruction kernel used in the study. It is important to note that there are other important parameters different among the patients which is not shown in the table (such as slice thickness, mAs and kVp etc.). Table 4. Breakdown of scanner vendors, models and reconstruction kernel used in the principal study. Vendor Model Reconstruction Kernel GE Medical LightSpeed VCT Lung GE Medical Revolution Lung GE Medical Discovery CT750 HD Lung Toshiba Aquilion FC52, FC56, FC09 Toshiba Aquilion one FC09 Siemens Somaton Definition Flash B60f, B70f 4.2.2 Training and test set A total of 79 patients (with 168 follow-up CT images) were included in the principal study. Figure 13 shows the schematic classification of the training and test sets. The training set consisted of 24 months median follow up with 16 recurrences and 31 non recurrences (47 patients in total). Independent validation sets were separated into three cohorts by follow-up CT scans at 24 months in cohort 1, 9 - 12 months in cohort 2 and 5 â 9 months in cohort 3. Cohort 1 contained 32 patients (22 non-recurrences and 10 recurrences), cohort 2 contained 27 patients (18 non-recurrences and 9 recurrences) and cohort 3 contained 30 patients (20 non-recurrence and 10 recurrence). Once the training and test set were separated, follow up CT images of the patients were extracted and anonymized. The anonymizing steps were identical to the pilot study in the previous chapter. 40 Figure 13. Schematic classification of training and test set. 4.2.3 Segmentation and feature extraction All the CT images were interpolated using Sitkspline function to 1mm Â´ 1mm Â´ 2.5mm and normalized to scale 0-1. These preprocessed images (n=168) were segmented using the GrowCut segmentation algorithm in 3D Slicer software.111 After the segmentation, radiomic feature extraction was completed with an open source python-based package pyradiomics.114 The settings used for pyradiomics are shown in Appendix A. Original sets of default 108 features were extracted from the seven radiomic feature classes; first order, shape, GLDM, GLCM, GLRM, GLSZM and NGTDM. 4.2.4 Feature harmonization and selection Three groups based on scanner vendors were created in the training set (i.e. General Electric, Siemens, Toshiba) and normalization was done on the radiomic features individually using the mean and standard deviation of the features for each of the individual scanner models. Breakdown of the scanner models and reconstruction kernels are shown in table 4. The validation set was individually normalized using only the center mean and standard deviation of the corresponding scanner in the training set. Figure 14 shows feature harmonization proposed in this research performed on one selected radiomic feature. The feature harmonization step was completed before the feature selection. 41 Figure 14. (a.) shows an unharmonized radiomic feature value and (b.) shows "harmonized" radiomic feature value based on the scanner vendors (0 â non-recurrence, 1 â recurrence). The feature selection was executed in python. Leave-one-out cross-validation (LOOCV) was used for feature selection. At each cross-validation fold, a forward feature selection with 1-nearest neighbor classifier was performed. N (number of patients) times 10 features were found at each fold and the top five most frequent features were selected to evaluate classification performance. 4.2.5 Classification using Support Vector Machine A support vector machine (nu algorithm, linear kernel) was used for classification. The gamma and cost settings were set to 0.5 and 4 respectively. The gamma and cost parameter were obtained by tuning the support vector machine using grid search algorithm.126, 127 The top five features selected using the training set and five features found in the previous research were evaluated for 24 months, 5-9 months and 9-12 months cohorts, as previously described. The statistics software R was used to build and evaluate the radiomics model.126,127 4.3 Results The top five radiomic features found from the training set were Dependence Non-Uniformity Normalized, Small Dependence Low Gray Level Emphasis, Run Length Non-Uniformity Normalized, Dependence Non-Uniformity and Small Dependence Emphasis (table 5). These features were evaluated on the training set using an internal cross validation approach, which included 3-folds, 5-folds, 10-folds and leave-one-out cross validation. An accuracy of 76.4%, 73.3%, 73.3%, 74.5% for 3-folds, 5-folds, 10-folds and leave-one-out cross validation were obtained respectively. This validation of the radiomic features on 42 different folds of the training set ensures stability and robustness of the features, prior to the evaluation of an independent validation set. Table 5. Selected radiomic features and the percentage of votes. Radiomic features Percentage of Votes (%) Dependence Non-Uniformity Normalized (GLDM) 43.6 Small Dependence Low Gray Level Emphasis (GLDM) 42.0 Run Length Non-Uniformity Normalized (GLRLM) 33.5 Dependence Non-Uniformity (GLDM) 33.5 Small Dependence Emphasis (GLDM) 32.9 Table 5 shows the selected radiomic features and the percentages of votes that each feature obtained using the feature selection approach. The top feature is Dependence Non-Uniformity Normalized with 43.6% of the votes. The percentages of votes for the top five radiomic features were obtained by counting the most often feature selected using the feature selection algorithm (see figure 9). The radiomic signature consisting of the top five features was evaluated in the three cohorts of follow up CT scans For validation cohort 1, the TPR was 70% and TNR was 100% (AUC = 0.85), for cohort 2, the TPR was 55.6% and TNR was 94.4% (AUC = 0.75), for cohort 3, the TPR was 70% and TNR was 90% (AUC = 0.80). As a comparison, the five radiomic features proposed by Mattonen et al. were also validated in the three independent cohorts. For cohort 1, the TPR was 50% and TNR was 77.3% (AUC = 0.64). For cohort 2, the TPR was 33.3% and TNR was 88.9% (AUC = 0.61). For cohort 3, the TPR was 20% and TNR was 75% (AUC = 0.48). The results showed that radiomics features found in the principal study outperforms the previously found radiomic features in all three independent validation sets, suggesting the lack of reproducibility in the radiomic features from one study to another. 43 4.4 Discussion The results from the research done by Mattonen et. al and our initial pilot study presented a need to have an independent validation set to assess the generalizability and predictive power of the radiomics model. In this chapter, a simple harmonization technique was implemented by splitting the dataset into three groups, based on scanner vendors, and normalization was performed individually across each group prior to feature selection. The harmonization method is easy to implement and performed well with our radiomics model. The performances of the five features found in this study were evaluated using an internal cross validation approach (training set) and external validation sets (test set). Both internal and external validation delivered satisfactory results in distinguishing a recurrence from non-recurrence. The previous study (Mattonen et al.) reported that the physicianâs performance differentiating a local recurrence from fibrosis only achieved a TPR of 1% and TNR of 99% within the 5 months follow-up post-SABR.89 This result shows that the physicians tend to classify the majority of the cases as non-recurrence during the early follow up period after SABR treatment. The radiomics assessment in our study indicated that a local recurrence can be detected at 5-9 months follow-up with a TPR of 70% and TNR of 90% (AUC = 0.80). In 9-12 months, follow-up, the TPR was slightly lower (55.6%) and TNR improved to 94.4%. demonstrating that the TNR is still consistently accurate whereas TPR performance decreased slightly. The final cohort of 24 months follow-up achieved a TNR of 100% and TPR of 70%, indicating that all non-recurrences were accurately classified. The performance of the radiomics model in the three cohorts necessitates further improvement in TPR. Nevertheless, the results obtained in the principal study showed that radiomics can be used as an additional tool for the physicians for the early detection of a local recurrence. The five radiomic feature signatures previously reported by Mattonen et al. were also tested in the principal study. The performance of these five radiomics features were not as favorable as reported in the previous study. The TPR and TNR were poorer, implying the lack of generalizability of their features in a new dataset. Nevertheless, it is important to note that there are significant differences in experimental design (including segmentation methods, pre-processing, feature extraction and feature standardization) between the two studies that may have caused the inconsistency in the results. Further validation of our own radiomic features needs to be completed before introducing them into clinical decision support system. Table 6 shows the list of anonymized patients who were either misclassified as recurrence or as non-recurrence. The parenthesis beside the anonymized ID shows the actual clinical outcome (R = recurrence, 44 NR = non-recurrence). The (â) shows that the patient was accurately classified as their true clinical outcome in the particular test set and (X) mark shows that the patient was not accurately classified. The table also summarizes the findings from the imaging report of the specific patients with routine CT follow-up and PET. As shown in the table, a greater number of patients who have local recurrences are misclassified as non-recurrence, therefore, the overall TPR performance of the radiomics model is poorer than the TNR performance (average of the 3 cohorts with TPR = 65.2%, TNR = 94.8%). The table shows that one patient out of the 7 misclassified patients are common across all three independent validation set. 4 patients out of 7 were misclassified in two validation sets. 2 patients were misclassified in only one validation set and both of them are in 5â9 months follow up cohort. The misclassification table also reveals another important limitation of radiomic features; the features predictive of a local recurrence in one follow-up cohort may not be predictive in another cohort, raising the question of stability in the performance. Table 6. The misclassified patient anonymized ID, the follow up scan (misclassified = X, accurately classified = (â) and the findings from medical imaging reports. Anonymized Patient ID 5-9 months 9-12 months 24 months CSI.SABR30 (R) (â) Post Radiation scarring (X) Unconfirming of a suspicious recurrence (X) Recurrence detected with PET imaging FVCC.SABR18 (R) (X) Post radiation scarring (X) Post radiation scarring (X) PET imaging suspicious of a local recurrence FVCC.fx.SABR4 (R) (â) Unconfirming of a recurrence (X) Highly concerning of a disease (X) Recurrence detected with PET imaging CSI.SABR169 (R) (X) Unconfirming of a recurrence (â) Unconfirming of a recurrence (â) Serial CT showed progression 45 Interestingly, the patient (FVCC.SABR18) misclassified in all the three test sets was scanned with CT image protocol with 5 mm slice thickness whereas the majority of the patients in the dataset were scanned with 2.5 mm slice thickness. Slice thickness like other parameters are significant and may influence the feature values calculated and a larger slice thickness corresponds to a lower resolution in the CT image. It is also important to note that not all the scans were performed with contrast-enhanced CT scans in the principal study. Some of the patients in our dataset (43 out of total 168 CT scans; 25.5% of the data) were scanned with a non-contrasted CT scan, for instance, FVCC.fx.SABR4 (in 24 months follow up) etc., which could possibly be the other cause of misclassification errors. In terms of cancer histology, 3 out of 7 misclassified patients have metastatic diseases arising from colon, colorectal and melanoma cancer. The rests of the 4 patients have presumed NSCLC, but with no histological confirmation. The main reason, for the lack of confirmation is that the immunohistochemistry test cannot be performed in those patients due to the lack of tissue samples. It is interesting to note that all the misclassified patients are either in the metastatic lung disease or in the unspecified NSCLC categories. Further investigations into the lack of generalizability of the radiomic features across metastatic disease and unspecified NSCLC must be performed in order to improve the misclassification rate. There are several key limitations associated with the current principal study. First, the current physicianâs assessment is relied on only one radiation oncologist. To find âa more accurateâ clinical outcome of the patients, more physicians must be enrolled into the study. A consensus among the physicians result in finding the âtrueâ pathological information of the patients. The other limitation is linked with segmentation. Even though a semi-automated segmentation tool was used, manual segmentation is considered to be the gold standard. A physicianâs approval may be beneficial in confirming the coverage VIC.SABR5 (R) (X) Mass identified suspected of a disease (X) Mass not seen distinctly (â) Recurrence detected with PET imaging VCC.fx.SABR49 (NR) (X) No suspicious finding of a local recurrence (X) No suspicious finding of a local recurrence (â) No suspicious finding of a local recurrence CSI.SABR129 (NR) (X) No evidence of a disease (â) Changes associated with fibrosis (â) Changes associated with fibrosis 46 of region of interest with the current segmentation. Lastly in terms of feature harmonization, a contrast-enhanced and non-contrasted CT scan must be separated in the groups to achieve a better performance. This could be achieved by involving more patients in the principal study. 4.5 Conclusion The principal study extends the ideas of the pilot study and has shown that when a radiomic feature harmonization is done, the features may possess the potential to detect a local recurrence and fibrosis at an earlier follow-up period after the SABR treatment. The analysis included 3 cohorts of a validation set, which all showed on average, an excellent TNR with a satisfactory TPR result. The radiomic features must be further validated by including more patients into the dataset. This type of tool based on radiomics, when properly translated into a clinical workflow, is non-invasive and could aid the physician in detecting a local recurrence accurately and as early as possible. 47 Chapter 5: Conclusion This chapter describes the overview and purpose of the research project, summary, limitations, short-term future work and long-term future work. 5.1 Overview of the research projects Stereotactic Ablative Radiotherapy (SABR) is a guideline recommended treatment for early-stage NSCLC patients.47 SABR has achieved high local control rates ranging from 88% to 96% at 2 to 4 years.62,64â67 However, one of its limitations is that the treated patients often develop a radiation-induced lung injury called fibrosis, which is similar to a local recurrence in terms of its morphological appearance and temporal evolution.65 Therefore, distinguishing a local recurrence from benign fibrosis early and accurately is essential for patients who may benefit from additional treatments. On the other hand, patients who have fibrosis but inaccurately misclassified as having a local recurrence may undergo unnecessary medical interventions and, in some cases, risky treatments.89 Currently post-SABR assessment focuses on qualitative high-risk features on CT FDG-PET imaging, both of which may take a year or more after the treatment to accurately detect a local recurrence. Therefore, an easy to use and non-invasive tool to accurately detect a local recurrence is highly relevant and urgently needed in a clinical workflow. Radiomics is an emerging area of research that is considered to be a promising tool in characterizing tissues of interest using quantitative image features. Radiomics has a huge potential in bridging the gap between medical imaging and personalized precision medicine.90 The application of radiomics stretches across different imaging modalities and across various cancer sites. A recent study has shown that radiomics may have potential in becoming a tool that can accurately classify patients with a local recurrence and fibrosis.89 However, the study reported the need to validate the radiomic features in a separate validation set since the features were only evaluated using an internal cross validation approach. The research objectives of the current thesis can be explained in two steps; first, to reproduce the previous research done by Mattonen et. al and validate their radiomic features. Second, to propose a new set of features and validate them in an independent validation set. These steps are essential to achieving the goal of building a radiomics pipeline that can automatically detect a local recurrence from the medical images as early and as accurately as possible in patients treated with SABR. 5.2 Summary of the chapters (2 and 3) The purpose of chapter 2 was to reproduce the radiomics study performed by Mattonen et al., which is the first-ever research published on implementing radiomics to distinguish a local recurrence from fibrosis in 48 post-SABR patients.89 Their study included 45 patients scanned with contrast-enhanced CT imaging protocol and used a lung reconstruction kernel. In the pilot study, two sets of radiomic feature signatures were evaluated; first set of radiomic features were found by Mattonen et al. in their study and second set of radiomic features were found using the feature selection approach proposed in their study with our dataset. To enable a direct comparison to the Mattonen et. al study, 28 patients (18 non-recurrences and 10 recurrences) with a similar image acquisition parameter (i.e. contrast-enhanced CT scan with lung reconstruction kernel) were selected from BC Cancer patient database. The two sets of radiomic features were evaluated using 2-folds, 3-folds, 5-folds and leave-one-out cross validation approach. The performances of radiomic features in the previous research was not comparable to the results found in pilot study suggesting a lack of reproducibility of the radiomic features. For leave-one-out cross validation, TPR rate of 0.20 and TNR of 0.28 were obtained using the radiomic features proposed in their research. The use of radiomic features found using their feature selection method improved the model performances approximately by a factor of two in leave-one-out cross validation (TPR = 0.40, TNR = 0.45). Chapter 3 is the principal study, which was an extension of the pilot study. As mentioned in the earlier sections, radiomic feature values are known to be influenced and affected by various image acquisition parameters and scanner models.96 This may pose challenges in comparing different radiomic studies and/or including patients from multiple centers. For that reason, a simple feature harmonization technique was proposed in chapter 3. Using the feature selection, five radiomic features were selected and evaluated in three independent validation sets grouped by follow-up period. The results obtained in the independent test sets were promising in the current application. Although the radiomic features were evaluated in validation sets, the TPR performance of the radiomic assessment could be further improved. Across the three validation sets, an average TPR of 65.2% and TNR of 94.8% (detail in chapter 3) were obtained. The principal study evaluated the previously reported radiomic features and found that the predictive power of those features was not significant in the current dataset (detail in chapter 3). 5.3 Limitations There are limitations surrounding both the pilot project and the principal study. The main limitation of the pilot project is the small size of the dataset. Due to radiomic features being intrinsically dependent on the scanner parameters, it is challenging to perform multi-centric studies; therefore, a subset of patients was selected from the entire database, based on a similar image acquisition parameter. The features were only tested using internal cross validation and therefore needed to be validated on an unbiased validation set. The radiomic feature performances in the pilot project was not comparable to the previous study by 49 Mattonen et al. highlighting the lack of reproducibility in radiomic features. However, it is important to note that the preprocessing steps in the radiomics studies were different, therefore it was not entirely a direct comparison. The principal study comprised of more patients than the pilot project, however the number of patients in the dataset could still be increased further. The current BC Cancer database contains 500 patients from which only 79 patients were included because those patients had the âconfirmationâ of their underlying pathology. By incorporating more physicians into the study, reviewing all 500 patients may be more feasible. One other important limitation of the principal study is that only one physicianâs assessment was included. More physicianâs findings must be incorporated to determine the âtrueâ pathology of all the patients. Furthermore, including more biopsy findings in the study may be useful since it is the gold standard method of detecting a recurrence. The majority of the underlying pathology of the patients in the principal study was determined with imaging techniques (high-risk features on CT and PET). 5.4 Future Work The chapters in the thesis validated the idea that radiomics may be a promising tool in distinguishing a local recurrence from fibrosis. This type of tool, if properly developed, may be incorporated into a daily clinical decision support system and aid physicians in making challenging decision that ultimately affect patient outcomes. However, there are numerous ways in which the current study can be improved even further. The future work can be separated into a short-term and long-term work. As previously described, the short-term goal after this study is to improve upon the principal study. One of the ways in which the quality of study could be improved is by incorporating more physicianâs assessments. Multiple physicians can assess if the patients have a local recurrence or not, thus a âtrueâ underlying pathology of a patient may be improved using a consensus-based assessment. There are improvements that can be made in terms of data analysis; various feature selection and machine learning algorithms are available for use in radiomics; different combination feature selection and classification may be investigated to improve the performance. Furthermore, different parameters within the specific machine learning algorithms may also be adjusted to improve the model. In terms of feature selection, features that may be correlated with one another could be eliminated from the model, for instance, dependence non-uniformity and dependence non-uniformity normalized may be correlated (one is the normalized version of the other). Radiomic features that donât necessarily add any additional 50 information and are redundant could potentially be removed to reduce the complexity of the model. However further investigation is needed in this research. One of the other areas of short-term future work could be improving our current feature harmonization technique. In the principal study, patients were grouped into different scanners irrespective of their imaging protocol. Further breakdown of scanner vendors by different imaging protocols could be implemented in future studies. This was not done in the principal study due to the limited number of patients in each imaging protocol. Nevertheless, separating the groups based on scanner vendors not only simplified the problem but it also delivered a satisfactory result. In future work, contrast-enhanced CT image and a non-contrasted CT image can be separated into groups given that each cohort contains adequate numbers of patients for harmonization, potentially reducing the misclassification error. For long-term goal, other areas of feature harmonization must be investigated. Radiomic feature harmonization is essential for CT images from multiple hospitals with diverse image acquisition parameters. A Phantom study is one of the ways in which radiomic features could be corrected, based on a specific scanner and imaging protocol.121 The Phantom study was shown to be effectively identify scanner specific effects on the radiomic feature values.121 This type of feature harmonization technique may reduce the misclassification error further, and could be used to find robust and reproducible radiomics biomarkers predictive of a local recurrence. The final goal of our future work is to create an automated radiomics pipeline that is easy to use and accurate. However, before incorporating the tool into a clinical workflow using a software-based system, the model must be validated in a prospective clinical trial. 51 Bibliography 1. Canadian Cancer Statistics Advisory Committee. Canadian Cancer Statistics 2019. Canadian Cancer Statistics 2019. (2019). 2. IARC. 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Voxel size and gray level normalization of CT radiomic features in lung cancer. Sci. Rep. (2018) doi:10.1038/s41598-018-28895-9. 61 Figure 15. (a.) shows the image matrix, in (b.) GLCM is calculated using the highlighted cooccurrences (i.e. 4 and 1, 3 and 4) in the image matrix. Figure 16. (a.) is the image matrix and (b.) is the GLSZM matrix calculated using highlighted size zones. Appendices Appendix A i.) GLCM calculation using pyradiomics toolbox: ii.) GLSZM calculation using pyradiomics toolbox: iii.) GLRLM calculation using pyradiomics toolbox: 62 Figure 17. (a.) shows the image matrix and (b.) shows the calculated GLRLM using highlighted run lengths. Figure 18. (a.) shows image matrix, (b.) shows the dependence matrix calculated using the values inside the box in image matrix (a.) iv.) GLDM calculation using pyradiomics toolbox: 63 Figure 19. (a.) shows the image matrix I with NGTDM calculated in (b.) v.) NGTDM calculation using pyradiomics toolbox: Note: All figures in Appendix A is adapted from pyradiomics documentation 64 Appendix B Pyradiomics settings code used in the principal and pilot study to enable reproducibility of study: setting: binWidth: 25 label: 1 interpolator: 'sitkBSpline' resampledPixelSpacing: [1,1,2.5] weightingNorm: None normalize : True normalizescale : 1 imageType: Original: {} featureClass: shape: firstorder: glcm: glrlm: glszm: gldm: ntgdm: 65 Appendix C Table 7. The previously found radiomic features and its corresponding implementation in pyradiomics for direct comparison. Previously Found Matlab Radiomics Feature Formula Radiomics Feature in Pyradiomics Library Formula Minimum gray-level The minimum intensity value First order Minimum The minimum intensity value GLCM Homogeneity HH P(i, j)1 + |i â j|!!"#$!!%#$ Inverse Difference H P&'(1 + k!)'$*#+ GLCM Correlation HH(i â µ%)Rj â µ"SP(i, j)Ï%Ï%!!"#$!!%#$ GLCM Correlation HHP(i, j)ij â µ&µ(Ï&(i)Ï((j)!!"#$!!%#$ GLCM Energy HHP(i, j),!!"#$!!%#$ GLCM Joint Energy HH(P(i, j)),!!"#$!!%#$ Gray Level Uniformity HP(ð¥),!"&#$ First order uniformity HP(i),!"&#$ For Matlab features column; the term P(i,j) is the GLCM matrix, Ng is the discrete gray level intensities and i, j is the ith and jth row of the GLCM matrix. Grey level uniformity is a first order feature where P(x) is the intensity of value of the voxels where Nl is the number of voxels within the region of interest. For pyradiomics features; Ng is the number of discrete gray level intensities in the image, P(i,j) is the GLCM matrix, i, j are the ith row and jth column of the GLCM matrix, μx, μy are the mean gray level intensities of px(i) and py(j), which are the marginal row (i) and column (j) probabilities and Ïx, Ïy are the standard deviation of the px and py. 66 Appendix D Table 8. Radiomic features and formula selected using the leave-one-out approach. Radiomic features Formula Size Zone Non-Uniformity Normalized (GLSZM) â (â (P(i, j)) !!%#$!!"#$ ,N-, Idmn (GLCM) H P&'((k)1 + (k,N),)!!'$*#+ Small Dependence Low Gray Level Emphasis (GLDM) â â P(i, j)i,j,!#"#$!!%#$ N- Small Area Low Gray Level Emphasis (GLSZM) â â P(i, j)i,j,!!"#$!!%#$ N- Coarseness (NGTDM) 1â p%s%!!%#$ If the radiomic feature is in GLSZM feature class (see table 8); Ng is the number of discrete intensity values in the image, Nz is the number of zones in the region of interest and P(i,j) is the size zone matrix. If the feature is in GLCM feature class; Ng is the number of discrete gray level intensities in the image, P(i,j) is the GLCM matrix, i, j are the ith row and jth column of the GLCM matrix, μx, μy are the mean gray level intensities of px(i) and py(j), which are the marginal row and column probabilities and Ïx, Ïy are the standard deviation of the px and py. For radiomic feature in GLDM class; Ng is the number of discrete intensity values, Nd is the discrete dependency sizes in the image, Nz is the number of dependency zone in the image and P(i,j) is the dependence matrix. For NGTDM class; Ng is the number of discrete intensity values, pi is gray level probability and si is the sum of absolute differences for gray level i. 67 Appendix E GLCM calculated in the following direction in the Mattonen et al. study: 68 Appendix F Table 9. Radiomic features, formula, percentage of votes and description. Radiomic features Formula Votes (%) Description Dependence Non-Uniformity Normalized (GLDM) â (â ð(ð, ð.$/#$ )),.%0#$ ð1, 43.6 Normalized version of dependence non-uniformity. Small Dependence Low Gray Level Emphasis (GLDM) â â ð(ð, ð)ð,ð,.%0#$.%/#$ ð1 42.0 Evaluates the joint distribution of small dependence with lower gray-level intensity values. Run Length Non-Uniformity Normalized (GLRLM) â [â ð(ð, ð|ð).$/#$ ],.&0#$ ð2(ð), 33.5 Measures the similarity in run-lengths in the image (run-lengths describes the consecutive pixels having the same intensity values). Lower value corresponding to greater similarity. Dependence Non-Uniformity (GLDM) â (â ð(ð, ð.$/#$ )),.%0#$ ð1 33.5 Evaluates the similarity of dependence throughout the image with lower value showing more homogeneity among dependencies. Small Dependence Emphasis (GLDM) â â ð(ð, ð)ð,.%0#$.$/#$ ð1 32.9 Evaluates the distribution of small dependencies. Larger value indicating small dependence and lesser homogenous texture. 69 For radiomic features in GLDM feature class, the variables are described in the previous section 3.3. For GLRLM feature class; Ng is number of discrete intensity values in the image, Nr is the discrete run lengths in the image along angle ð and P(ð,ð|ð) is the run length matrix for an arbitrary direction.
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Medical Physics
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Radiomics for early detection of tumour recurrence in patients treated with lung stereotactic ablative radiotherapy (SABR)
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