Medicine, Faculty of
Non UBC
Radiology, Department of
DSpace
BC Cancer Research Centre. Integrative Oncology
Hasanabadi, Setareh
Aghamiri, Seyed Mahmud Reza
Abin, Ahmad Ali
Abdollahi, Hamid
Arabi, Hossein
Zaidi, Habib
Lymphoma, encompassing a wide spectrum of immune system malignancies, presents significant complexities in its early detection, management, and prognosis assessment since it can mimic post-infectious/inflammatory diseases. The heterogeneous nature of lymphoma makes it challenging to definitively pinpoint valuable biomarkers for predicting tumor biology and selecting the most effective treatment strategies. Although molecular imaging modalities, such as positron emission tomography/computed tomography (PET/CT), specifically ¹⁸F-FDG PET/CT, hold significant importance in the diagnosis of lymphoma, prognostication, and assessment of treatment response, they still face significant challenges. Over the past few years, radiomics and artificial intelligence (AI) have surfaced as valuable tools for detecting subtle features within medical images that may not be easily discerned by visual assessment. The rapid expansion of AI and its application in medicine/radiomics is opening up new opportunities in the nuclear medicine field. Radiomics and AI capabilities seem to hold promise across various clinical scenarios related to lymphoma. Nevertheless, the need for more extensive prospective trials is evident to substantiate their reliability and standardize their applications. This review aims to provide a comprehensive perspective on the current literature regarding the application of AI and radiomics applied/extracted on/from ¹⁸F-FDG PET/CT in the management of lymphoma patients.
Citation: Hasanabadi, S.; Aghamiri,S.M.R.; Abin, A.A.; Abdollahi, H.;Arabi, H.; Zaidi, H. EnhancingLymphoma Diagnosis, Treatment, andFollow-Up Using 18F-FDG PET/CTImaging: Contribution of ArtificialIntelligence and Radiomics Analysis.Cancers 2024, 16, 3511. https://doi.org/10.3390/cancers16203511Academic Editor: AhmadShariftabriziReceived: 5 September 2024Revised: 11 October 2024Accepted: 15 October 2024Published: 17 October 2024Copyright: © 2024 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).cancersReviewEnhancing Lymphoma Diagnosis, Treatment, and Follow-UpUsing 18F-FDG PET/CT Imaging: Contribution of ArtificialIntelligence and Radiomics AnalysisSetareh Hasanabadi 1 , Seyed Mahmud Reza Aghamiri 1, Ahmad Ali Abin 2, Hamid Abdollahi 3,4 ,Hossein Arabi 5 and Habib Zaidi 5,6,7,8,*1 Department of Medical Radiation Engineering, Shahid Beheshti University, Tehran 1983969411, Iran;setareh.hasanabadi@gmail.com (S.H.); smr-aghamiri@sbu.ac.ir (S.M.R.A.)2 Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran 1983969411, Iran;a_abin@sbu.ac.ir3 Department of Radiology, University of British Columbia, Vancouver, BC V5Z 1M9, Canada;habdollahi@bccrc.ca4 Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada5 Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva,Switzerland; hossein.arabi@unige.ch6 Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University MedicalCenter Groningen, 9700 RB Groningen, The Netherlands7 Department of Nuclear Medicine, University of Southern Denmark, 500 Odense, Denmark8 University Research and Innovation Center, Óbuda University, 1034 Budapest, Hungary* Correspondence: habib.zaidi@hug.ch; Tel.: +41-22-372-7258Simple Summary: Lymphoma is a type of cancer that affects the immune system and can be difficultto diagnose and treat effectively, especially in the early stages. Current imaging methods, suchas PET/CT scans, are valuable tools for diagnosing and monitoring the disease, but they havelimitations in providing precise information for personalized treatment plans. Recently, radiomicsand artificial intelligence (AI) have emerged as promising technologies that can analyze detailedpatterns in medical images, helping to uncover information that might not be visible to the humaneye. This study explores the potential of these technologies in improving the diagnosis, staging, andtreatment selection for lymphoma patients. However, further research is needed to confirm theirreliability and ensure they can be effectively used in clinical practice.Abstract: Lymphoma, encompassing a wide spectrum of immune system malignancies, presentssignificant complexities in its early detection, management, and prognosis assessment since it canmimic post-infectious/inflammatory diseases. The heterogeneous nature of lymphoma makes itchallenging to definitively pinpoint valuable biomarkers for predicting tumor biology and selectingthe most effective treatment strategies. Although molecular imaging modalities, such as positronemission tomography/computed tomography (PET/CT), specifically 18F-FDG PET/CT, hold sig-nificant importance in the diagnosis of lymphoma, prognostication, and assessment of treatmentresponse, they still face significant challenges. Over the past few years, radiomics and artificialintelligence (AI) have surfaced as valuable tools for detecting subtle features within medical imagesthat may not be easily discerned by visual assessment. The rapid expansion of AI and its applicationin medicine/radiomics is opening up new opportunities in the nuclear medicine field. Radiomicsand AI capabilities seem to hold promise across various clinical scenarios related to lymphoma.Nevertheless, the need for more extensive prospective trials is evident to substantiate their reliabilityand standardize their applications. This review aims to provide a comprehensive perspective on thecurrent literature regarding the application of AI and radiomics applied/extracted on/from 18F-FDGPET/CT in the management of lymphoma patients.Cancers 2024, 16, 3511. https://doi.org/10.3390/cancers16203511 https://www.mdpi.com/journal/cancersCancers 2024, 16, 3511 2 of 38Keywords: lymphoma; 18F-FDG PET/CT; radiomics; genomics; radiogenomics; deep learning;personalized therapy1. IntroductionA variety of diseases that result from the clonal proliferation of lymphocytes areknown as malignant lymphomas [1]. These conditions can be classified into two primarytypes: Hodgkin lymphoma (HL) and non-Hodgkin lymphoma (NHL). HL comprises arelatively small portion, accounting for approximately 10% of all lymphoma cases, whileNHL constitutes the majority, representing roughly 90% of all lymphoma cases. WhileT cells, or natural killer cells, can also be responsible for lymphomas, the majority (90%)originate from B cells [2]. In 2023, NHL is expected to account for 4.1% of new cancerdiagnoses and 3.3% of cancer-related deaths in the United States of America, while HLis projected to represent 0.5% of new diagnoses and 0.1% of deaths during the sameperiod [3,4].Positron emission tomography combined with computed tomography using[18F]-fluorodeoxyglucose (18F-FDG PET/CT) is widely employed as a noninvasive three-dimensional imaging modality in the management of lymphoma patients. 18F-FDGPET/CT has different indications in lymphoma patients in various aspects, includinginitial staging before treatment, re-staging, evaluation after treatment, monitoring therapyprogress, follow-up post-therapy, and assessing disease transformation. However, it isimportant to acknowledge that this technology has limitations, including variations in FDGavidity among various lymphoma subtypes, as well as the potential for false-negative andfalse-positive results [5,6].Radiomics is a rapidly advancing research field focused on quantitatively extractingfeatures from medical images and transforming them into rich and high-dimensional data.The AI learning process begins with data collection and preprocessing, followed by featureextraction, model training, and evaluation. The analysis of these data provides uniquebiological insights, enhancing our understanding of disease processes and supporting clini-cal decision-making [7–9]. Furthermore, 18F-FDG PET/CT radiomics enhances diagnosis,facilitates the development of individualized treatment strategies, and refines outcomepredictions based on factors like tumor heterogeneity and lymphoma’s biological, patho-logical, and metabolic characteristics. This non-invasive technique has gained significantattention in the clinical setting owing to its versatile applications [10,11].In recent years, the integration of artificial intelligence (AI) into the field of medicalimaging has had a profound impact on patient care, disease detection, diagnosis, andtreatment planning [12–15]. AI algorithms enable the segmentation of medical images,automated analysis, disease identification, risk assessment, therapy response assessment,and prognosis prediction [16–19]. AI also optimizes image quality, allowing for quantita-tive analysis of imaging biomarkers, and provides clinicians with decision-support toolsthrough the integration of various patient data [20]. Leveraging these cutting-edge methodscan lead to improved lymphoma diagnosis and treatment, resulting in more successfulpersonalized medicine strategies [21].Despite the continuous growth of AI research in the medical field, numerous challengesrequire further investigation and research, particularly with respect to the availability ofdiverse and extensive datasets and multi-center validation studies.The objective of this study is to evaluate how radiomics and AI tools using 18F-FDGPET/CT can effectively enhance the accuracy of lymphoma diagnosis, optimize treatmentstrategies, and improve disease monitoring. This assessment will focus on the role ofAI in delivering personalized treatment options and ultimately leading to better clinicaloutcomes for patients with lymphoma.Cancers 2024, 16, 3511 3 of 382. Literature Search StrategyTo conduct this review, we undertook a comprehensive exploration of the PubMeddatabase using a diverse set of search phrases and keywords. These search terms included‘lymphoma’, ‘artificial intelligence’, machine learning’, ‘radiomics’, ‘deep learning’, and‘radiogenomics’. Our search covered content from the year 2000 to 20 August 2024. Weused the Boolean operators ‘AND’ and ‘OR’ to combine the main terms and keywords,refining the search results. Publications covering a wide range of diseases, conferencepapers, literature reviews, and articles in non-English languages were excluded usingspecific criteria to narrow down our study selection. A limitation of this study is the useof a single database (PubMed) for the bibliography search, which may have limited theinclusion of studies available in other databases. It is important to note that a limitation ofthis study is the use of a single database (PubMed) for the bibliography search, which mayhave limited the inclusion of studies available in other databases.3. Literature Search OutcomePrimarily, in the first step of our research, 227 articles were selected. After rigorouslyapplying the specified inclusion/exclusion criteria outlined in the Section 2, we meticu-lously identified a total of 128 articles that were considered relevant. It is important tonote that the majority, amounting to up to 95% of the research, was conducted using aretrospective design. These findings, along with a detailed summary of the studies, can befound in Tables 1 and 2.As expected, the number of studies using radiomics and/or AI has significantly risensince 2020, and the keywords ‘Lymphoma’, ‘PET’, and ‘Radiomics’ together seem to leadto more relevant results (Figure 1). In the subsequent step, we meticulously categorizedthe studies into five distinct sections, as visually represented in Figure 2. The majority ofstudies were related to the topics of “progression and outcome prediction.” In contrast,genomics studies comprised the smallest share of the research.As a result, our findings revealed that the predominant approach in lymphoma re-search involves employing radiomics or a combination of radiomics with AI for diseaseprogression and outcome prediction of patients. Notably, similar to other research in thisdomain, these studies employed a retrospective design.Cancers 2024, 16, x FOR PEER REVIEW 3 of 42 2. Literature Search Strategy To conduct this review, we undertook a comprehensive exploration of the PubMed database using a dive se s t of s arch phrases and keywords. These search terms included ‘lymphoma’, ‘artificial intelligence’, machine learning’, ‘radiomics’, ‘deep l arning’, and ‘radiogenomics’. Our search covered conte t from the ye r 2000 to 20 August 2024. We used th Boolean operato s ‘AND’ and ‘OR’ o combine the main terms and keywords, refining the search results. Publications covering a wide range of diseases, confer nce pa-p rs, literature review , and articles in on-English languages were exclud d using spe-cific c iteria to narrow down our study selection. A limitation of this study is the use of a single database (PubMed) for the bibliography search, which may have limited the inclu-sion of studies v ilable in other databases. It is import nt to note that a limitation of this study is the use of a single database (PubMed) for the bibliography search, which may have limited the inclusion of studies available in other databases. 3. Literature Search Outcome Primarily, in the first step of our research, 227 articles were selected. After rigorously applying the specified inclusion/exclusion criteria outlined in the Section 2, we meticu-lously identified a total of 128 articles that were considered relevant. It is important to note that the majority, amounting to up to 95% of the research, was conducted using a retro-spective design. These findings, along with a detailed summary of the studies, can be found in Tables 1 and 2. As expected, the number of studies using radiomics and/or AI has significantly risen since 2020, and the keywords ‘Lymphoma’, ‘PET’, and ‘Radiomics’ together seem to lead to more relevant results (Figure 1). In the subsequent step, we meticulously categorized the studies into five distinct sections, as visually represented in Figure 2. The majority of studies were related to the topics of “progression and outcome prediction.” In contrast, genomics studies comprised the smallest share of the research. As a result, our findings revealed that the predominant approach in lymphoma re-search involves employing radiomics or a combination of radiomics with AI for disease progression and outcome prediction of patients. Notably, similar to other research in this domain, these studies employed a retrospective design. Figure 1. Bar chart showing the number of publications according to years (the search was con-ducted from the year 2000 to 20 August 2024. No articles were found on PubMed prior to 2013). 01020304050607080902013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024Number of publicationsYearKeywords: "Lymphoma" and "PET"and "Radiomics"Keywords: "Lymphoma" and "PET"and "Machine Learning"Keywords: "Lymphoma" and "PET"and "Deep Learning"Keywords: "Lymphoma" and "PET"and "Artificial Intelligence"Figure 1. Bar chart showing the number of publications according to years (the search was conductedfrom the year 2000 to 20 August 2024. No articles were found on PubMed prior to 2013).Cancers 2024, 16, 3511 4 of 38Cancers 2024, 16, x FOR PEER REVIEW 4 of 42 Figure 2. A pie chart illustrating the distribution of publications based on the study subject. 4. Summary of Findings and Discussion 4.1. From Image to Clinical Decision: Potential of 18F-FDG PET/CT Radiomics in Lymphoma 18F-FDG PET/CT is widely utilized for lymphoma staging, diagnosis, and treatment guidance today [5] . However, it has limitations due to increased FDG uptake in organs infiltrated post-immunochemotherapy, infections, and inflammation. Furthermore, lymphoma may not consistently exhibit high uptake, which complicates therapy response assessment [22,23]. These challenges present difficulties for physicians in image interpretation and clinical decision-making . With the growing demand for advanced diagnostic tools in lymphoma management encompassing various clinical scenarios, such as early and precise diagnosis, staging, prognosis assessment, and treatment response evaluation, the fields of radiomics and AI have increasingly come to the forefront of lymphoma research. In the upcoming sections, we will explore the potential benefits of 18F-FDG PET/CT radiomics and AI-powered tools in diverse clinical situations. 4.1.1. Diagnosis and Risk Assessment of Bone Marrow Infiltration (BMI) Evaluation of bone marrow infiltration in lymphomas has significant effects on prognosis and therapeutic management. The gold standard procedure for this assessment has historically been bone marrow biopsy (BMB). Concurrently, 18F-FDG-PET has been recognized as an important staging tool for lymphomas and may serve as a non-invasive BMB supplant or replacement. Ongoing research and debates are still being conducted on the clinical use of 18F-FDG-PET to assess BMI in lymphomas [24]. To identify BMI in patients with lymphoma, radiomic features extracted from 18F-FDG PET/CT images provide an additional tool for visual assessment [25]. In this regard, Faudemer et al. demonstrated the potential of FDG-PET/CT radiomics for diagnosing BMI in Follicular lymphoma (FL) patients [26]. Furthermore, when comparing diagnostic methods, they identified a significant difference between BMB and visual PET assessments (p-value = 0.010). However, there was no significant difference between BMB and PET pred.score assessments (based on radiomic features), as depicted in Figure 3. To enhance the diagnostic capabilities of FDG-PET in this context, further exploration of skeleton texture analysis is recommended. In a comprehensive study involving 82 patients diagnosed with Diffuse Large B Cell Lymphoma (DLBCL), Aide et al. identified that the radiomics feature SkewnessH, a first-order metric, demonstrated a sensitivity of 81.8% and specificity of 81.7% in accurately 60%13%12%7%5% 3%progression and outcomepredictionSystem-Aided diagnosis andstagingImaging-Based BiomarkerspredictionDiagnosis and Risk Assessmentof Bone Marrow InfiltrationHistologic SubtypeDifferentiationGenomicsFigure 2. A pie chart illustrating the distribution of publications based on the study subject.4. Summary of Findings and Discussion4.1. From Image to Clinical Decision: Potential of 18F-FDG PET/CT Radiomics in Lymphoma18F-FDG PET/CT is widely utilized for lymphoma staging, diagnosis, and treatmentguidance t day [5]. However, it has limitations due to increased FDG uptake in organs infil-trated post-immunochemotherapy, infections, and inflammatio . Furthermore, lymphomamay not consistently exhibit high uptake, which complicates therapy response assess-ment [22,23]. These challenges present difficulties for physicians in image interpretationand clinical decision-making.With the growing demand for advanced diagnostic tools in lymphoma managementencompassing various clinical scenarios, such as early and precise diagnosis, staging,prognosis assessment, and treatment response evaluation, the fields of radiomics and AIhave increasingly come to the forefront of lymphoma research. In the upcoming sections,we will explore the potential benefits of 18F-FDG PET/CT radiomics and AI-powered toolsin diverse clinical situations.4.1.1. Diagnosis and Risk Assessment of Bone Marrow Infiltration (BMI)Evaluation of bone marrow infiltration in lymphomas has significant effects on prog-nosis and therapeutic management. The gold standard procedure for this assessmenthas historically been bone marrow biopsy (BMB). Concurrently, 18F-FDG-PET has beenrecognized as an important staging tool for lymphomas and may serve as a non-invasiveBMB supplant or replacement. Ongoing research and debates are still being conductedon the clinical use of 18F-FDG-PET to assess BMI in lymphomas [24]. To identify BMIin patients with lymphoma, radiomic features extracted from 18F-FDG PET/CT imagesprovide an additional tool for visual assessment [25].In this regard, Faudemer et al. demonstrated the potential of FDG-PET/CT radiomicsfor diagnosing BMI in Follicular lymphoma (FL) patients [26]. Furthermore, when compar-ing diagnostic methods, they identified a significant difference between BMB and visualPET assessments (p-value = 0.010). However, there was no significant difference betweenBMB and PET pred.score assessments (based on radiomic features), as depicted in Figure 3.To enhance the diagnostic capabilities of FDG-PET in this context, further exploration ofskeleton texture analysis is recommended.In a comprehensive study involving 82 patients diagnosed with Diffuse Large B CellLymphoma (DLBCL), Aide et al. identified that the radiomics feature SkewnessH, a first-order metric, demonstrated a sensitivity of 81.8% and specificity of 81.7% in accuratelydetecting BMI in baseline FDG PET/CT [27]. This study underscored the pivotal role ofskeletal textural characteristics in BMI diagnosis and the imp ovement of risk as essmentfor DLBCL patients. Conver ely, in another study involving DLBCL patients, no significantdifferences were observed in textural features extracted from the femurs and iliac regionsCancers 2024, 16, 3511 5 of 38between patients with positive and negative BMB patients [28]. However, two parametersderived from the grey-level zone-length matrix in the iliac crests exhibited a tendencyfor higher values in the BMB-positive group compared to the BMB-negative group. It isimportant to note that these differences did not reach statistical significance after applyingmultiple comparison corrections. The limited number of patients in the study may haveinfluenced the research outcomes. Incorporating larger sample sizes and conductingrepeated studies will enhance the accuracy and reliability. In an effort to demonstrate thevalue of FDG-PET radiomics in predicting BMI in mantel cell lymphoma (MCL) patients, ithas been found that FDG-PET texture features significantly enhanced SUV-based prediction(AUC 0.73 vs. 0.66). Furthermore, combining these radiomic features with laboratory datacan further enhance the predictive performance (AUC = 0.81) [29].Cancers 2024, 16, x FOR PEER REVIEW 5 of 42 detecting BMI in baseline FDG PET/CT [27]. This study underscored the pivotal role of skeletal textural characteristics in BMI diagnosis and the improvement of risk assessment for DLBCL patients. Conversely, in another study involving DLBCL patients, no signifi-cant differences were observed in textural features extracted from the femurs and iliac regions betw en patients with positive nd negative BMB patients [28]. Ho ever, two pa-ramet rs derived from the grey-level zone-length ma rix n the iliac crests exhibi d a ten-dency for higher values in the BMB-positive gr up compared to the BMB-negative group. It is important to note at these differences did not reach statist cal sig ific nce fter ap-plying multiple comparison c rrections. Th limited number of patients in the stud may have i fluenced the research outcomes. Incorporating larger sample sizes a ti r t st i s ill t r r li ilit . I ff rt t str t t al e f - ra i ics in predicting BMI in mantel cell lymphoma (MCL) patients, it has been found that FDG-PET texture features significantly enhanced SUV-base pre-diction (AUC 0.73 vs. 0.66). Furthermore, combining these radiomic features with labora-tory data can further enhance the predictive performance (AUC = 0.81) [29] . Figure 3. ROC curves for BMI diagnosis through BMB, visual PET, skewness_HISTO PET, and pred.score PET evaluations. (a) Comparative ROC curves; (b) evaluation of sensitivity, specificity, PPV, and NPV; (c) rates of true positives, true negatives, false positives, and false negatives. Re-printed with permission from [26] under a Creative Commons Attribution 4.0 International License. In another remarkable study, Sadik et al. proposed an innovative AI-based approach aimed at detecting bone marrow uptake (BMU) in HL patients [30]. Their findings re-vealed an impressive concordance rate (81%) between the AI’s evaluation of BMI and as-sessments by physicians. Furthermore, AI quantified diffuse BMU, showcasing its poten-tial for clinical application and demonstrating performance comparable to the manual method, as illustrated in Figure 4. Similarly, in a related study, employing AI to highlight suspicious BMU in HL patients significantly enhanced interobserver agreement among physicians from various hospitals [31]. Figure 3. ROC curves for B I diagnosis through B B, visual PET, skewness_HISTO PET, andpred.score PET evaluations. (a) Comparative ROC curves; (b) evaluation of sensitivity, specificity,PPV, and NPV; (c) rates of true positives, true negatives, false positives, and false negatives. Reprintedwith permission from [26] under a Creative Commons Attribution 4.0 International License.In another remarkable study, Sadik et al. proposed an innovative AI-based approachaimed at detecting bone marrow uptake (BMU) in HL patients [30]. Their findings revealedan impressive concordance rate (81%) between the AI’s evaluation of BMI and assessmentsby physicians. Furthermore, AI quantified diffuse BMU, showcasing its potential forclinical application and demonstrating performance comparable to the manual method, asillustrated in Figure 4. Similarly, in a related study, employing AI to highlight suspiciousBMU in HL patients significantly enhanced interobserver agreement among physiciansfrom various hospitals [31].Despite the prognostic value of FDG-PET/CT radiomics in accurately detecting BMIand enhancing risk assessment in lymphoma patients, only a limited number of studieshave explored its full potential up to now [11,25–31]. Due to the importance of BMI inlymphoma patients, especially B-cell lymphoma [B-cell lymphoblastic lymphoma 40–60%,small lymphocytic lymphoma 85%, MCL 50–80%, FL 40–70%], and in Aggressive NK cellleukemia (>95%) and also in Lymphocyte depleted (>50%) [32], it is necessary to conductfurther research in the field. Future research can focus on various subtypes of lymphomaor larger and diverse datasets and explore its clinical utility.Cancers 2024, 16, 3511 6 of 38Cancers 2024, 16, x FOR PEER REVIEW 6 of 42 Figure 4. FDG-PET/CT representation: An instance where both AI-driven technique and the major-ity (8 out of 10) of physicians identified localized uptake in the skeletal/bone marrow (indicated in red and with arrows). Reprinted with permission from [30] under a Creative Commons Attribution 4.0 International License. Despite the prognostic value of FDG-PET/CT radiomics in accurately detecting BMI and enhancing risk assessment in lymphoma patients, only a limited number of studies have explored its full potential up to now [11,25–31]. Due to the importance of BMI in lymphoma patients, especially B-cell lymphoma [B-cell lymphoblastic lymphoma 40–60%, small lymphocytic lymphoma 85%, MCL 50–80%, FL 40–70%], and in Aggressive NK cell leukemia (>95%) and also in Lymphocyte depleted (>50%) [32], it is necessary to conduct further research in the field. Future research can focus on various subtypes of lymphoma or larger and diverse datasets and explore its clinical utility. 4.1.2. Histologic Subtype Differentiation: Radiomics and Future Directions The current gold standard for diagnosing most hematopoietic and lymphoid tissue malignancies still involves the pathological examination of the afflicted tissue, typically obtained through surgical resection [33]. There are several pitfalls associated with this approach, including errors arising from insufficient data/tissue acquisition, personal sub-jectivity, and complexities in the classification and diagnosis of lymphomas, as well as errors related to molecular genetic tests [34] . Moreover, the invasive nature of this method, Figure 4. FDG-PET/CT representation: An instance where both AI-driven technique and the majority(8 out of 10) of physicians i en ified localized uptake in the skeletal/bone m rrow (indicated in redand with arrows). Reprinted with permission from [30] under a Creative Commons Attribution 4.0International License.4.1.2. Histologic Subtype Differentiation: Radiomics and Future DirectionsThe current go d stand rd for diagnosing most hem topoietic and lymphoid tissuemalignancies still involves the pathological examination of the afflicted tissue, typicallyobtained through surgical resection [33]. There are several pitfalls associated with thisapproach, including errors arising from insufficient data/tissue acquisition, personal sub-jectivity, and complexities in the classification and diagnosis of lymphomas, as well aserrors related to molecular genetic tests [34]. Moreover, the invasive nature of his method,along with the fact that it may ot adequately capture relap ed or refractory cases, cansignificantly impact treatment decisions.Different subtypes of bulky mediastinal lymphomas, such as classical HL (CHL), grayzone lymphoma, and primary mediastinal B-cell lymphoma (PMBCL), exhibit distinctFDG-PET characteristics and metabolic heterogeneity. Machine learning (ML) combinedwith radiomics can help in creating a visual representation of these masses to aid in thehistological differentiation of mediastinal bulky lymphomas [35].In a retrospective investigation carried out by de Jesus et al. involving 120 patients,it was found that a radiomics-driven model exhibited superior performance compared toSUVmax-based models when it came to discriminating between FL and DLBCL (area underthe curve (AUC) of 0.86 vs. 0.79, accuracy of 80% vs. 0.70) [36]. Furthermore, Lovinfosse et al.reported a moderate level of agreement among observers in the diagnosis of HL versusDLBCL [37]. The best ML models for differentiating DLBCL from HL showed promisingresults (AUC of 0.95 in the lesion-based approach using TLR (tumor-to-liver ratio) radiomics,and an AUC of 0.86 in the patient-based approach utilizing original radiomics and age).Cancers 2024, 16, 3511 7 of 38Yang et al. successfully developed DL-CAD systems based on PET/CT images toaddress the clinical challenge of differentiating histological components in enlarged cervicallymph nodes, particularly between lymph node metastasis and lymphoma involvement [38].Their findings demonstrated that combining handcrafted and DL-based features improveddiagnostic performance. The integration of these systems in clinical practice holds promisefor improving therapeutic intervention quality and optimizing patient outcomes. Recentresearch emphasized the usefulness and potential of radiomic features and machine learning-based radiomics/deep learning models in distinguishing between different tumor types,such as primary CNS lymphoma (PCNLS) and glioblastoma [39], breast DLBCL and breastinvasive ductal carcinoma (IDC) [40], breast carcinoma from breast lymphoma [41], pancreaticcarcinoma from mass-forming pancreatic lymphoma [42], differentiation between thymicepithelial tumors (TETs) and mediastinal lymphomas (MLs) [43], lymphoma from liverand lung cancers [44], sarcoidosis and malignant lymphoma [45], renal cell carcinoma andrenal lymphoma [46], squamous cell carcinoma and NHL in the oropharynx [47], and inenhancing the diagnostic performance for PCNLS and brain metastases [48], and sarcoidosisand lymphoma [37,45]. A limited number of studies focused on distinguishing the histologicalsubtypes of lymphoma itself. For differentiating Diffuse Large B-Cell Lymphomas (DLBCLs)and mucosa-associated lymphoid tissue (MALT) lymphomas, Total Lesion Glycolysis (TLG)achieved an AUC of 0.906, sensitivity of 0.900, and specificity of 0.780 [49].A recent study introduced PST-Radiomics, a method that enhances lymphoma classifi-cation by capturing intra-tumor metabolic heterogeneity (ITMH) using PET/CT imaging.This approach outperformed traditional methods in subtype classification by analyzingtumor segments with a specialized neural network [50].Future studies should focus on differentiating other subtypes using multiclass classifica-tion ML and Deep Learning (DL) algorithms. Including larger datasets and external validationwill enhance the reliability of this approach compared to traditional biopsy methods.4.1.3. Progression and Outcome PredictionCurrently, 18F-FDG PET/CT stands as the standard imaging technology for diagnosing,staging, and predicting prognosis in both HL and NHL patients. It has been widelyused in managing lymphoma patients, and its importance in prognostication is steadilygrowing [51]. Additional insights obtained from 18F-FDG PET/CT scans, particularlythrough radiomics-based analysis to assess tumor heterogeneity, have the potential toenhance prognostic accuracy. The integration of radiomics and AI into prediction prognosisassessment is under investigation, either in combination with existing prognostic toolsor as standalone markers. These advancements aim to facilitate more precise prognosispredictions and enhance clinical decision-making for patients with lymphoma. In thissection, we will explore the application of AI and radiomics in lymphoma risk stratification.Diffuse aggressive NHL in adults can achieve complete remission in a substantialpercentage, ranging from 60% to 80%. However, a challenging aspect of managing thisdisease is that from 20% to 40% of patients may experience relapses. Similarly, HL re-sponds well to chemotherapy, leading to potential cures for the majority, but from 5%to 10% may have refractory disease initially, and from 10% to 30% may relapse [52,53].Despite the existence of clinical prognostic tools, like the International Prognostic Index(IPI) for risk assessment, accurate prognosis prediction remains a challenge due to thesignificant heterogeneity within lymphoma. NHL encompasses various subtypes, eachwith distinct characteristics, making it complex to identify risk factors that contribute toour understanding of underlying biological mechanisms and clinical practice [54,55].The majority of studies conducted in the field of progression and outcome predictionhave focused on DLBCL due to its widespread occurrence and significant clinical importancewithin the field of lymphomas [2]. A combined clinical and PET/CT radiomics model demon-strated superior performance compared to a model solely based on metabolic tumor volume(MTV) (AUC = 0.75 vs. AUC = 0.67) when it came to predicting 2-year event-free survival(2-EFS) for DLBCL patients who received R-CHOP treatment [56]. Another study suggestedCancers 2024, 16, 3511 8 of 38that integrating AI-generated imaging metrics into clinical workflows enhances risk stratifica-tion and improves central nervous system relapse prediction in DLBCL, surpassing the IPI[progression-free survival (PFS): 1.87 vs. 1.38; overall survival (OS): HR = 2.16 vs. 1.40] [57].It is worth noting that a novel radiomics feature selection approach has effectivelydemonstrated lesion heterogeneity within HL patients. The findings by Sollini et al. un-derscore the importance of considering multiple lesions for accurate patient outcomeprediction, as relying solely on the largest lesion was found to be unreliable [58].Another research effort aimed to predict PFS in patients with CHL after salvage chemother-apy and autologous stem-cell transplant [59]. A predictive machine learning model was de-veloped using 18F-FDG PET radiomics and clinical data for 113 classical Hodgkin lymphomapatients, identifying high-risk individuals with lower 3-year PFS. The model achieved AUCs of0.810 in the training cohort and 0.750 in the validation cohort. Adding radiomic features suchas MTV and TLR, SUVmean improved performance, while run-length non-uniformity was anindependent survival predictor in DLBCL patients [60]. Moreover, when combined with theIPI, it forms a valuable risk stratification model, guiding personalized treatment strategies.Attempts to improve the prediction of OS and PFS in DLBCL patients using hybridnomograms that combine the radiomics score (RS) with IPI showed promising results. Itseems that the application of radiomics analysis to metabolic bulk volume (MBV) has emergedas a potential approach for assessing prognosis [61]. In a study performed on aggressiveB-cell lymphoma, certain radiomic features, notably gray-level non-uniformity (GLNU)and kurtosis—measuring the “tailedness” of the intensity distribution within lymphomatumors on 18F-FDG PET/CT scans; high kurtosis indicating a sharp peak and heterogeneousmetabolic activity, while low kurtosis suggesting uniform activity—were correlated with OS(p = 0.035). Tumor texture was unable to forecast the response to treatment [62].In another study with DLBCL patients who were treated with the RCHOP regime, PETradiomics showed promise for prognosis [63]. Radiomic features, including aRS, stronglypredicted PFS, cause-specific survival (CSS), and OS in the testing and validation cohorts, withcorresponding AUCs of 0.709, 0.721, 0.740, and 0.706, 0.763, and 0.703, respectively. UtilizingPET/CT radiomics, a research effort established a predictive nomogram for assessing DLBCLpatients’ PFS during immunochemotherapy, incorporating RS, blood platelet levels, andgender [64]. A recent study presented a promising multimodal deep learning model forpredicting treatment failure in DLBCL patients using PET/CT imaging, achieving a highaccuracy (91.22%) and an AUC of 0.926 in the primary dataset while maintaining strongperformance (88.64% accuracy and an AUC of 0.925) in the external dataset [65].Employing machine learning in PET-based radiomics showed potential for predictingDLBCL treatment outcomes. This includes SUVmax and Gray-Level Co-Occurrence Matrix(GLCM) dissimilarity, which served as independent predictors for response to therapy [66].In another study, involving 271 DLBCL patients, a combined model was developed toidentify high-risk patients after first-line therapy. This model incorporated clinical features,baseline, end-of-therapy (EoT) PET radiomic features, and delta PET features. The studydemonstrated superior prognostic performance compared to traditional models, such asthe International Prognostic Index (IPI) and Deauville score (DS), offering improved riskstratification and enhanced clinical decision support [67]. In a dual-center retrospectiveanalysis of DLBCL patients, endeavors to predict 2-EFS using radiomics and clinical datayielded promising results (AUC of 0.85) [68].In a study conducted by Jiang et al., the authors developed a PET radiomics signature(Rad-Sig) to predict DLBCL patient survival [69]. The study’s results demonstrated a signif-icant association between Rad-Sig and both PFS and OS. Models that integrated Rad-Sig incombination with clinical factors exhibited superior performance compared to other models(C-index of 0.801 for PFS, 0.807 for OS). Moreover, it should be emphasized that externalvalidation confirmed the effectiveness of these models in predicting patient survival.An additional investigation has identified the predictive potential of PET radiomicfeatures in primary gastrointestinal DLBCL patients obtaining treatment with an R-CHOP-like regimen. These investigations have revealed that the incorporation of Rad-Sig togetherCancers 2024, 16, 3511 9 of 38with metabolic and clinical variables can robustly forecast patient outcomes with greateraccuracy when compared to clinical models and the IPI score, underscoring its value infacilitating personalized treatment decisions [70].Eertink et al. [71] emphasized that integrating baseline radiomics with commonly usedclinical predictors significantly enhances the prediction of treatment outcomes. This com-bination improved model performance, resulting in a 15% increase in positive predictivevalue (PPV), and allowed for more precise identification of high-risk patients comparedto the IPI model. The clinical predictors in this study included key factors such as WHOperformance status and age above 60 years. As shown in Figure 5, the IPI model achievedan AUC of 0.68, the optimal radiomics model reached an AUC of 0.76, and the combinedmodel, incorporating both radiomic and clinical features, achieved an AUC of 0.79.Cancers 2024, 16, x FOR PEER REVIEW 10 of 42 Figure 5. Top panel: Maximum intensity projections showcasing patients categorized by MTV and dissemination levels. Tumors are marked in red. Sequence from left to right: high MTV with high dissemination, low MTV with high dissemination, high MTV with low dissemination, and low MTV with low dissemination. Bottom panel: Receiver operating characteristic curves for the 2-year pro-gression timeframe based on IPI, best clinical approach, MTV, the top radiomics model, and com-bined prediction models. Modified and reprinted with permission from [71] under a Creative Com-mons Attribution 4.0 International License. The research conducted by Eertink and other colleagues underscores the enhanced precision in identifying high-risk patients through the utilization of baseline 18F-FDG-PET radiomic features, surpassing the conventional IPI risk score [72]. Furthermore, their clin-ical PET model, developed from the HOVON-84 dataset, consistently demonstrated its predictive efficacy across six independent studies. Irrespective of the selected lesion or feature selection techniques, Eertink, in collabo-ration with other researchers, found that patient-level conventional PET features and dis-semination features exhibited the most substantial predictive potential for DLBCL patient progression within a 2-year period (AUC range of 0.72–0.75). Nevertheless, textural and morphological radiomic features did not contribute to improving the predictive value Figure 5. Top panel: Maximum intensity projections showcasing patients categorized by MTV anddissemination levels. Tumors are marked in red. Sequence from left to right: high MTV with highdissemination, low MTV with high dissemination, high MTV with low dissemination, and lowMTV with low diss minati . Bottom panel: Receiver operating characteristic curv s for the 2-yearprogression timeframe based on IPI, best clinical approach, MTV, the top radiomics model, andcombined prediction models. Modified and reprinted with permission from [71] under a CreativeCommons Attribution 4.0 International License.Cancers 2024, 16, 3511 10 of 38The research conducted by Eertink and other colleagues underscores the enhancedprecision in identifying high-risk patients through the utilization of baseline 18F-FDG-PETradiomic features, surpassing the conventional IPI risk score [72]. Furthermore, theirclinical PET model, developed from the HOVON-84 dataset, consistently demonstrated itspredictive efficacy across six independent studies.Irrespective of the selected lesion or feature selection techniques, Eertink, in collab-oration with other researchers, found that patient-level conventional PET features anddissemination features exhibited the most substantial predictive potential for DLBCLpatient progression within a 2-year period (AUC range of 0.72–0.75). Nevertheless, textu-ral and morphological radiomic features did not contribute to improving the predictivevalue [73]. Incorporating first- and second-order radiomic features in a PET scan modelenhanced outcome prediction for early-stage Hodgkin lymphoma, as indicated in Figure 6.This approach also held promise for identifying patients with a higher risk of mortality,pointing toward personalized risk-adapted management (AUC = 95.2%) [74].Cancers 2024, 16, x FOR PEER REVIEW 11 of 42 [73]. Incorporating first- and second-order radiomic features in a PET scan model en-hanced outcome prediction for early-stage Hodgkin lymphoma, as indicated in Figure 6. This approach also held promise for identifying patients with a higher risk of mortality, pointing toward personalized risk-adapted management (AUC = 95.2%) [74]. Figure 6. Top panel: Receiver Operating Curves showcasing the performance of models incorporat-ing five radiomic characteristics (in red), metabolic tumor volume (in blue), total lesion glycolysis (in black), and GlobalMax (SUVmax, represented in green) for the patient group with mediastinal conditions. Bottom panel: Heatmap illustrating the prognostic categories derived from the top five mediastinal radiomic predictors. Modified and reprinted with permission from [74] under a Crea-tive Commons Attribution 4.0 International License. Using deep learning and rule-based reasoning, a recent study predicted treatment response in B-cell lymphomas based on pre-treatment CT and PET images, surpassing traditional methods (in the lesion-based approach on one whole slice, AUC = 0.93 for PET vs. 0.91 for diagnostic quality CT and 0.92 for low-dose CT) [75]). Another investigation focusing on DLBCL patients revealed that the utilization of maximum intensity projection (MIP) images from 18F-FDG PET baseline scans with a CNN model enhanced the predic-tion of 2-year time-to-progression (TTP) when compared to IPI (AUC of 0.74, vs. 0.68) [76]. A radiomics-based model for predicting 2-EFS in cHL patients is feasible. The most effective model in their dataset was built using ridge regression. Nevertheless, no notable Figure 6. Top panel: Receiver Operating Curves showcasing the performance of models incorporatingfive radiomic characteristics (in red), metabolic tumor volume (in blue), total lesion glycolysis (inblack), and GlobalMax (SUVmax, represented in green) for the patient group with mediastinalconditions. Bottom panel: Heatmap illustrating the prognostic categories derived from the top fivemediastinal radiomic predictors. Modified and reprinted with permission from [74] under a CreativeCommons Attribution 4.0 International License.Cancers 2024, 16, 3511 11 of 38Using deep learning and rule-based reasoning, a recent study predicted treatmentresponse in B-cell lymphomas based on pre-treatment CT and PET images, surpassingtraditional methods (in the lesion-based approach on one whole slice, AUC = 0.93 for PETvs. 0.91 for diagnostic quality CT and 0.92 for low-dose CT) [75]. Another investigationfocusing on DLBCL patients revealed that the utilization of maximum intensity projection(MIP) images from 18F-FDG PET baseline scans with a CNN model enhanced the predictionof 2-year time-to-progression (TTP) when compared to IPI (AUC of 0.74, vs. 0.68) [76].A radiomics-based model for predicting 2-EFS in cHL patients is feasible. The mosteffective model in their dataset was built using ridge regression. Nevertheless, no notabledistinction was observed between this model and a logistic model that included MTVand clinical features. However, further research is required to establish optimal clinicalthresholds [77]. The pre-treatment 18F-FDG PET/CT radiomics model effectively predictedibrutinib treatment response in diverse lymphoma patients, outperforming traditional PETmetrics [78]. Combining intra-tumoral heterogeneity features, PET metrics, and clinical riskfactors improves survival predictions for untreated DLBCL patients compared to usingonly the IPI. The prognostic model, when stratified by cell-of-origin (COO) subgroups,identified a high-risk population with lower 2-year survival rates compared to the IP [79].Specific combinations of PET and CT radiomics with clinical parameters independentlypredicted patient survival and the need for radiotherapy in HL patients [80]. On the otherhand, in another study involving extranodal natural killer (NK)/T cell lymphoma (ENKTL)patients, a radiomics-dependent model demonstrated lower performance when comparedto the metabolism-based model. The inferior performance observed could be attributed tothe retrospective study design or the small dataset size [81].Zhao et al. demonstrated that the integration of 18F-FDG PET radiomics and clini-cal data using stacking ensemble learning could enhance risk stratification in DLBC [82].The study found that radiomics features like high-intensity run emphasis (HIR) and run-length nonuniformity (RLNU) from PET and CT are independent predictors of treatmentresponse in HL patients. Additionally, zone-size nonuniformity (ZSNU), intensity nonuni-formity (INU), and short run emphasis (SRE) were identified as independent predictorsof survival [83]. It has been shown that machine learning techniques using an iteratedcross-validation methodology improve the predictive value for determining PFS and OS inDLBCL patients [59,84].A multi-center study on 240 DLBCL patients using a stacking ensemble learning with18F-FDG PET radiomics in combination with clinical data demonstrated superior perfor-mance in predicting PFS (AUC: 0.771, accuracy: 0.789) and OS (AUC: 0.725, accuracy: 0.763),offering better risk stratification than clinical factors alone and the International PrognosticIndex [82].Additionally, a study evaluated the predictive value of PET-based radiomics for CAR T-cell therapy outcomes in relapsed or refractory large B-cell lymphoma (LBCL). The authorsreported that the radiomics signature had an AUC of 0.73, outperforming traditionalbiomarkers such as total TMTV with an AUC of 0.66 and SUVmax with an AUC of 0.59.Higher radiomic scores were linked to longer PFS and OS [85].Combining PET texture features, specifically histogram-based features (HISTO_EntropyPET), with the German Hodgkin Group (GHSG) clinical stage effectively pre-dicted resistance to first-line therapy in Hodgkin Lymphoma with mediastinal bulk. Thisapproach achieved over 70% accuracy and produced distinct progression-free survivalcurves [86].In relapsed or refractory (R/R) DLBCL patients undergoing CAR-T cell therapy,the combined radiomics and clinical model outperformed the clinical model alone, withAUCs of 0.776 vs. 0.712 for PFS and 0.828 vs. 0.728 for OS in the training cohort, and0.886 vs. 0.635 for PFS and 0.778 vs. 0.705 for OS in the validation cohort [87]. The combinedmodel integrating radiomics, clinical variables, and conventional PET parameters showedsuperior performance in predicting outcomes for DLBCL with extranodal involvement,achieving C-indices of 0.724 for PFS and 0.842 for OS [88].Cancers 2024, 16, 3511 12 of 38For extranodal nasal-type NK/T cell lymphoma (ENKTCL), the combined clinical+ metabolic + radiomics model showed superior predictive performance with Harrell’sC-index of 0.805 for PFS and 0.833 for OS in the validation cohort, outperforming othermodels [89]. In patients with DLBCL, baseline PET/CT features describing tumor spreadand dissemination in relation to the spleen strongly predicted survival. Combining thesefeatures with TMTV and IPI enhanced the prediction of survival even more [90].Machine learning models, particularly those combining MTV and MTVrate (quotientof the largest lesion’s volume and total body MTV), provided the most accurate predictionof progression-free survival in DLBCL, surpassing individual assessments [91]. The com-bined model incorporating RadScore, sex, B symptoms, and SUVmax significantly improvedthe prediction of medium-term efficacy and prognosis in high-risk DLBCL patients com-pared to individual assessments [92]. Compared to SUV-based features, 18F-FDG PET/CTradiomic models provided better screening results and high accuracy for T-cell lymphomain children [93].Draye-Carbonnier et al. [94] found that combining TMTV, tumor volume surfaceratio (TVSR), and median distance between tumors (medPCD) from baseline 18F-FDGPET/CT improved prognosis prediction in high-burden follicular lymphoma. Moreover,integrating clinical and PET-derived features significantly improved the prediction of PFSand OS in DLBCL patients, aiding in early risk identification and personalized treatmentstrategies [95].The study by Bodet-Milin et al. found that radiomic features from baseline 18F-FDGPET/CT, particularly from the lesion with the highest uptake, had better prognostic valuefor predicting minimal residual disease (MRD) in mantle cell lymphoma (MCL) patientsthan clinical and laboratory parameters [96]. In contrast, the GAINED study indicated thatadding PET and radiomic features did not improve predictions of survival and diseaseprogression beyond clinical and treatment features alone. While clinical features achieveda mean AUC of 0.72 ± 0.06 for 2-year PFS, and PET and radiomic features had lower AUCsof 0.60 ± 0.07 and 0.62 ± 0.07, respectively. This suggests that PET and radiomic featuresmay not add significant value, underscoring the need for further research to optimize theirintegration into clinical prediction models [97].Due to the limited reliability of tumor size in predicting treatment response in lym-phoma, exploring alternative prognostic markers like radiomics is advisable. However,small sample sizes may affect study findings [3]. While these predictive models could bevaluable, further research is needed. Future studies should focus on larger, diverse datasetsand emphasize external validation with multi-institutional datasets and different imagereconstruction algorithms. Additionally, the retrospective bias in past studies highlightsthe need for more prospective research.4.2. Connecting Genes and Images: Radiogenomics PerspectiveRadiogenomics, a promising field in oncology, holds significant potential in precisionmedicine. It integrates extensive radiomic features extracted from medical images, geneticinformation obtained through high-throughput sequencing, and clinical–epidemiologicaldata into mathematical models. This fusion of radiomics and genomics offers an avenue toenhance our understanding of the molecular mechanisms underlying tumor development.Furthermore, it provides valuable insights to identify unique traits in cancer patients,facilitate clinical decision-making by predicting prognosis, and advance the creation ofpersonalized treatment strategies [4].Despite the importance of combining radiomics and genomics, there has been limitedresearch in the context of lymphoma. In this section, we review these studies and highlightpotential avenues for future research in this area. In the following section, we discuss theapplication of radiogenomics in the management of lymphoma.In an effort to establish a strong connection between chemoresistance and alteredmetabolism in DLBCL, researchers successfully identified a 6-gene metabolic signature(T-GEP). A predictive Rad-Sig that was produced by combining FDG-PET radiomicsCancers 2024, 16, 3511 13 of 38and T-GEP data was significantly correlated with metabolic gene expression profiling(GEP)-based signature (r = 0.43, p-value = 0.0027), and it was also associated with PFS(p-value = 0.028) [98]. This highlights FDG-PET radiomics’ potential as a noninvasive toolfor determining cancer metabolism and improving prognostic precision in DLBCL patients.Additionally, combining imaging, clinical, and genomic information successfully predictedtreatment response in DLBCL patients, with BCL6 amplification and particular radiomicfeatures serving as important determinants [99].The study led by Patricio et al. employs advanced ML algorithms to analyze geneexpression profiles, aiming to predict treatment responses in CHL patients after twochemotherapy cycles. Through the use of feature selection techniques, they successfullyidentified 14 genes (such as CD9, CD79B, BCL10, and others) strongly associated withpost-chemotherapy FDG-PET results [100]. In summary, these results signify improvedtreatment response prediction in HL disease while emphasizing the ongoing need forfurther advancements in clinical applicability.A study with a small number of B-cell lymphoma patients reported that the combineduse of 18F-FDG PET/CT radiomics and genomic factors provided added value in predictingthe survival prospects of BCL patients receiving CAR T-cell treatment [101]. However, itis worth noting that no significant association was observed between PET/CT variablesand the occurrence of cytokine release syndrome. Imaging biomarkers, such as SUVmaxderived from FDG, exhibited a moderate and approximately equal correlation with theProliferation Index Ki-67 in lymphomas [102]. In addition, significant correlations werefound between blood-based biomarkers and PET features in 30 cHL patients, specificallyMTV and dissemination [103]. Future research should investigate combining blood-basedbiomarkers with FDG-PET radiomics to improve treatment response and prognosis predic-tion in cHL and other lymphoma subtypes. It is recommended to further investigate therelationship between radiomic features and the Ki-67 Proliferation Index.As discussed in Section 4.1, promising research focused on predicting treatmentresponse and survival using radiomics. However, there have been limited studies exploringthe potential integration of radiomics with genomic information.4.3. Future Prospects in Genomics and Radiomics: Liquid Biopsy and Radiomics in LymphomaFDG-PET is a crucial component in the management of malignant lymphomas [6].Meanwhile, circulating tumor DNA (ctDNA), an emerging biomarker, provides genotypicinsights and evaluates treatment effectiveness by detecting minimal residual disease (MRD)in lymphomas [104]. Quantitative, mutational, and fragmentation features in ctDNAdynamically predict treatment response and survival, capturing the complete lymphomaecosystem that extends beyond traditional approaches [105]. Combining LiqBio-MRDwith PET/CT results in strong diagnostic accuracy for identifying FL patients with rapidprogression, underscoring LiqBio-MRD’s value in early high-risk patient identification andprecision therapy [106].Radiomics and liquid biopsy offer significant promise in oncology due to their mini-mally invasive nature, ease of execution, and potential for repeated assessments duringpatient follow-ups. These methods allow for the extraction of valuable insights into tu-mor type, aggressiveness, progression, and treatment response [107]. Recognizing theimportance of liquid biopsy, it is essential to emphasize the need for further research incancer diagnosis and treatment. Future research should prioritize exploring the correlationbetween radiomic features and ctDNA, potentially offering a comprehensive understand-ing of lymphoma progression and treatment response, ultimately benefiting patient careand outcomes.Cancers 2024, 16, 3511 14 of 38Table 1. Summary of radiomics and AI-based radiomics studies conducted on lymphoma using 18F-FDG-PET/CT images. All studies included in this Table areretrospective, except Ceriani et al. [63], which is prospective in its validation. This distinction highlights one of the key limitations of the research landscape, asretrospective studies may introduce biases that can affect the interpretation of the findings.Study Year P.N Task Study Design Feature ExtractionSoftware Type Classifier Results (Based on Best Model) Key FindingsAide et al. [27] 2018 82 BMI Diagnosis,OS, PFS Retrospective LifeX (version 2.0) DLBCLStatisticalanalysis Sensitivity 81.8%, specificity 81.7%SkewnessH was notable feature foridentifying BMI and onlyindependent predictor for PFSMayerhoeferet al. [29] 2020 97 BMI Diagnosis RetrospectiveBeth IsraelPET/CT viewer MCL MLRad-Sig (SUV and GLCM features):AUC = 0.73Rad-Sig with laboratory data: AUC = 0.81Combining radiomics texturefeatures and laboratory dataenhances BMI prediction in MLC.Faudemeret al. [26] 2021 66 BMI Diagnosis Retrospective LifeX (version 5.1) FLStatisticalanalysisSensitivity: 70.0%, Specificity: 83.3%, PPV77.8%, NPV: 76.9%, AUC: 0.822Skeletal textural shows potentialpromise for the diagnosis BMI.Predictive features: variance (GLCM),correlation (GLCM), joint entropy(GLCM) and busyness (NGLDM)Sadiket al. [30] 2021 153 BMI Diagnosis Retrospective - HLDL andstatisticalanalysisAgreement between AI method andphysicians:81%An AI-driven approach effectivelyidentifies BMU in HL.Kenawyet al. [25] 2020 44 BMI Diagnosis RetrospectiveCGITA toolbox(Math Works Inc.,Natick, MA, USA,version 2015a)UnspecifiedsubtypeStatisticalanalysisHILRE, HILZE, LRE, LZE, max spectrum,busyness, and code similarity:AUC > 0.682, p < 0.05Univariate analysis:significant predictors of BMI were codesimilarity and LRE: p = 0.039, p = 0.02respectively.Multivariate analysis:significant predictor of BMI was LRE:p = 0.031BMI diagnosis is improved by thecombination of 18F-FDG PET/CTradiomic features (particularly LRE)and visual evaluation.Sadiket al. [31] 2022 48 BMI Diagnosis Retrospective - HL DLCorrelation between physicians’decisions (using AI vs. not using it):Mean Kappa = 0.51 (0.25–0.80) vs. 0.61(0.19–0.94)An AI-based model significantlyenhances consensus amongphysicians from various hospitals byhighlighting suspicious focal BMUsin HL.de Jesuset al. [36] 2022 120SubtypesDifferentiation RetrospectivePyRadiomics(version 3.0)FL andDLBCL ML AUC: 0.86, accuracy: 80%ML analysis of radiomic features hasthe potential to offer diagnostic valuein differentiating FL from DLBCLtumor lesions, surpassing thediagnostic capabilities ofSUVmax alone.Cancers 2024, 16, 3511 15 of 38Table 1. Cont.Study Year P.N Task Study Design Feature ExtractionSoftware Type Classifier Results (Based on Best Model) Key FindingsLovinfosseet al. [37] 2022 251SubtypesDifferentiation Retrospective RadiomiX ToolboxDLBCL andHL MLLesion-based approach using TLRradiomics: AUC = 0.95patient-based approach (utilizing originalradiomics and age): AUC = 0.86Differentiating HL from DLBCLtumors can be achieved using MLand radiomic features.Yanget al. [38] 2023 165Differentiationlymph nodemetastasis vs.lymphomainvolvement.Retrospective -Lymph nodemetastasisandlymphomainvolvementDL AUC = 0.901, accuracy = 86.96%,sensitivity = 76.09%, specificity = 94.20%DL-based CAD systems exhibitpromising diagnostic capabilities indistinguishing between metastaticand lymphomatous involvement inenlarged cervical lymph nodes.Froodet al. [56] 2022 229 2-EFS RetrospectivePyRadiomics(version 2.2.0) DLBCL MLCombine model (radiomic featuresand clinical):AUC (validation vs. test) = 0.75 vs. 0.73Integrating clinical characteristics withradiomic features exhibits potential inpredicting 2-EFS outcomes.Solliniet al. [58] 2020R/R:85Non-R/R:76Classifying/R vs.non-R/R Retrospective LifeX (version 4.9) HLStatisticalanalysisConsidering all lesions vs. one singlelesion: Accuracy: 82% vs. 62%,Sensitivity: 70% vs. 78%, Specificity: 88%vs. 53%The information obtained fromvarious lesions contributes more topredicting patient outcomescompared to relying solely on thelargest lesion.Lue et al. [60] 2020 83 PFS, OS Retrospective OsiriX DLBCL StatisticalanalysisPFS (RLNGLRLM): HR = 15.7, p = 0.007OS (RLNGLRLM): HR = 8.64, p = 0.040RLNGLRLM serves as an independentradiomics feature for predictingsurvival outcomes.Parvezet al. [62] 2018 82Response toTherapy, DFS, OS Retrospective LifeXaggressiveB-celllymphomaStatisticalanalysisDFS(GLNU): p = 0.013, OS (kurtosis):p = 0.035first-line therapy response: not significantThere is a significant correlationbetween GLNU and DFS, whilekurtosis is correlated with OS, tumortexture features could not predictresponse to Therapy.Cerianiet al. [63] 2022 133 PFS, OSRetrospective(in validation)Prospective(in test)PyRadiomics DLBCL StatisticalanalysisPFS (training vs. validation):AUC: 0.709 vs. 0.706OS: AUC: 0.740 vs. 0.703CSS: AUC: 0.721 vs. 0.763Rad-Sig has the potential to improverisk stratificationLi et al. [64] 2022 129 PFS Retrospective LifeX (version6.10) DLBCLStatisticalanalysisAUCs for PFS (training vs. validation set):1 year: 0.79 vs. 0.67, 2 years: 0.84 vs. 0.83,5years: 0.88 vs. 0.72Combining the Rad-Score withclinical characteristics can be used topredict the outcome.Cancers 2024, 16, 3511 16 of 38Table 1. Cont.Study Year P.N Task Study Design Feature ExtractionSoftware Type Classifier Results (Based on Best Model) Key FindingsYuanet al. [65] 2022 249 PTF Retrospective - DLBCL DLPrimary dataset: accuracy: 91.22%,AUC: 0.926External dataset: accuracy: 88.64%,AUC: 0.925The DL-based model demonstratedrobust performance, validated theprognostic significance of interimPET/CT scans, and offers potential forinforming personalized treatments.Coskunet al. [66] 2021 45Response toTherapy RetrospectiveLifeX(version 5.10) DLBCLML andstatisticalanalysisAccuracy: 87%AUC = 81%GLCM dissimilarity: p = 0.001Greater baseline PET image texturalheterogeneity was linked toincomplete treatment response.GLCM dissimilarity wereindependent predictor forincomplete responseCui et al. [67] 2023 271 2Y-TTP,PFS Retrospective Pyradiomic DLBCLML, statisticalanalysis2Y-TTP: AUC (training vs. validation vs.test) = 1.00 vs 0.83 vs. 0.898Sensitivity (training vs. validation vs.test): 100% vs. 80.5% vs. 94.1%Specificity (training vs. validation vs.test): 100% vs. 79.2 vs. 78.1PFS: C-index: 0.853Integrating clinical data along withbaseline, EoT, and delta PET featuresimprove prognosis for progression/relapse after first-line therapy.Ritteret al. [68] 2022 85 2-year EFS RetrospectiveInterViewFUSION(version 3.10)DLBCL ML, statisticalanalysisCenter 1:sensitivity = 79%, specificity = 83%,PPV = 69%, NPV = 89%,center 2 (evaluating) = 0.85 AUCPredicting 2-year EFS throughradiomics and image features (Dmax,neighbor gray tone difference matrix(NGTDM) busyness, TLG, TMTV, andNGTDM coarseness) is achievable.Jianget al. [69] 2022 283 OS, PFS Retrospective PyRadiomics DLBCLStatisticalanalysisPFS: C-index of 0.801OS: C-index of 0.807External validation:PFS:C-index of 0.758OS: C-index of 0.794Combining Rad-Sig clinical factors, itcould enable precise risk stratification.Jianget al. [70] 2022 140 OS, PFS Retrospective Pyradiomics PGI-DLBCLStatisticalanalysisCombine model (training vs. validation):PFS: (0.825 vs. 0.831), OS: (0.834 vs. 0.877)Combining radiomics, clinical, andmetabolic parameters into a unifiedpredictive model improve the accuracyof predicting both PFS and OS.Cancers 2024, 16, 3511 17 of 38Table 1. Cont.Study Year P.N Task Study Design Feature ExtractionSoftware Type Classifier Results (Based on Best Model) Key FindingsEertinket al. [71] 2022 317Response toTherapy Retrospective RaCat DLBCLStatisticalanalysisCombination of radiomics and clinicalfeatures: AUC 0.79progression at 2-year TTP.: HR = 4.6Adding radiomics to clinicalpredictors improved high-risk patientselection (2-year TTP progression:44% vs. 28%), increasing PPV by 15%compared to the IPI model.Eertinket al. [73] 2022 296predicting 2-yearprogression. Retrospective RaCat DLBCLStatisticalanalysis AUC = 0.72–0.75Patient-level conventional PETfeatures and dissemination featuresare the most valuable predictors for2-year progression. Textural andmorphological radiomic featuresdid not contribute to improving thepredictive value.Travainiet al. [108] 2023 112 OS, PFS RetrospectiveLifeX(version 4.00) DLBCLStatisticalanalysisPFS: HR = 3.56 (95% CI: 2.29–5.54, p < 0.0001),C-index of 0.84 (95% CI: 0.77–0.91)OS: HR = 3.83 (95% CI: 2.37–6.20, p < 0.0001),C-index of 0.90 (95% CI: 0.81–0.98).Radiomic features, especially whenintegrated with clinical factors,demonstrated strong predictivecapabilities for both PFS and OS inDLBCL patients.Milgromet al. [74] 2019 251Predict RefractoryMediastinal HL RetrospectiveImagingBiomarkerExplorer (basedon version 8.1.0,MathWorks)HL ML AUC = 95.2%The model utilizing PET dataincludes both first-order andsecond-order radiomic features,outperforms MTV and TLG alone,offering improved risk stratification.Froodet al. [77] 2022 289 2-year EFS Retrospective PyRadiomics cHL MLTraining, validation and test: AUCs = 0.82,0.79 and 0.81, respectivelyIt is feasible to predict outcomesusing radiomic features integratedwith ML model.Jimenezet al. [78] 2022 169Response toTherapy Retrospective3D Slicer software(PyRadiomicsextention)UnspecifiedlymphomasubtypeStatisticalanalysisResponse to ibrutinib treatment:AUC: 0.860, Sensitivity: 92.9%,Specificity: 81.4%at the patient level: AUC = 0.811.validated in treatment subgroups:first line: AUC = 0.916 s or greater line:AUC = 0.842single treatment: AUC = 0.931Multiple treatments: AUC = 0.824The radiomics model demonstratedsuperior predictive accuracy forresponse to ibrutinib therapy.Cancers 2024, 16, 3511 18 of 38Table 1. Cont.Study Year P.N Task Study Design Feature ExtractionSoftware Type Classifier Results (Based on Best Model) Key FindingsKostakogluet al. [79] 2022 1263 PFS and OS RetrospectivePORTS radiomicstoolkit DLBCLStatisticalanalysisAll patients (N = 1263):PFS: AUC = 0.74, OS: AUC = 0.92Survival probabilities at 2 years (low,intermediate, high risk)PFS: 94%, 72%, 54%OS: 100%, 100%, 51COO subgroup analysis (n = 832):PFS: AUC = 0.86, OS: AUC = 0.89PFS: 88%, 86%, 45%OS: 91%, 93%, 65%PET-based prognostic model,(incorporating radiomics, PETmetrics, and clinical factors)outperforms the traditional IPI inpredicting survivalZhaoet al. [82] 2023 240 PFS and OS Retrospective LifeX (version 6.3) DLBCL MLPFS: AUC = 0.771, accuracy = 0.789.OS: AUC = 0.725 and an accuracy of 0.763.The combined model using 18F FDGPET radiomics and clinical dataimproves risk stratification inDLBCL patients.Wanget al. [109] 2020 110 PFS and OS Retrospective LifeX (version 5.1) ENKTLStatisticalanalysisPFS (training vs. validation):Radiomics-based Model:C-index = 0.811 vs. 0.588Metabolism-based Model:C-index = 0.751 vs. 0.693OS (training vs. validation):Radiomics-based Model:C-index = 0.818 vs. 0.628Metabolism-based Model:C-index = 0.828 vs. 0.753.R-signature:PFS: HR = 3.56 (95% CI: 2.29–5.54, p = 0.001),C-index of 0.84 (95% CI: 0.77–0.91)OS: HR = 3.83 (95% CI: 2.37–6.20, p = 0.032),C-index of 0.90 (95% CI: 0.81–0.98).Combining Rad-Sig and clinicalfactors demonstrate robustpredictive capabilities.In the validation phase, theradiomics-based modeldemonstrated lower effectivenesscompared to themetabolism-based models.Lue et al. [83] 2020 35Response toTherapy, OS,PFSRetrospective CGITA(MATLAB 2012a) HLStatisticalanalysisTreatment Response Prediction:HIR_GLRMPET: OR = 36.4, p = 0.014RLNU_GLRMCT: OR = 30.4, p = 0.020PFS:INU_GLRMPET: HR = 9.29, p = 0.023Wavelet SRE_GLRMCT: HR = 18.40,p = 0.012OS: ZSNU_GLSZMPET: HR = 41.02, p = 0.001A prognostic stratification model:PFS (p < 0.001), OS (p < 0.001).Predictive features for treatmentresponse HIR_GLRMPET andRLNU_GLRMCT.Independent predictive features forsurvival: ZSNU(GLSZMPET),INU(GLRMPET), and wavelet SRE(GLRMCT)Cancers 2024, 16, 3511 19 of 38Table 1. Cont.Study Year P.N Task Study Design Feature ExtractionSoftware Type Classifier Results (Based on Best Model) Key FindingsChanget al. [84] 2023 122 PFS, OS Retrospective - DLBCL ML and DLBest Predictive Models for PFS:accuracy 0.71, Sensitivity: 0.58,Specificity: 0.84, AUC: 0.71, PPV: 0.75,NPV: 0.71, F1 score: 0.62Best Predictive Model for OS:accuracy of 0.76, Sensitivity: 0.59,Specificity: 0.84, AUC: 0.72, PPV: 0.75,NPV: 0.75, F1 score: 0.66ML methods, utilizing clinical andmetabolic features, improve PFS,OS prediction.Zhanget al. [61] 2021 152 PFS, OS RetrospectiveLifeX(version 6.30) DLBCLStatisticalanalysisPFS (training vs. validation) (all p < 0.05):AUCs of TMTV-based hybrid nomogram(combining RS with IPI) 0.828 vs. 0.783AUCs of MBV-based hybrid nomogram:training 0.835 vs. 0.787OS (all p < 0.05):AUCs of TMTV-based hybridnomogram: 0.818 vs. 0.789AUCs of MBV-based hybrid nomogram:0.831 vs. 0.792The hybrid nomograms utilizingRad-Sig with IPI could significantlyimprove survival prediction.Mazzaraet al. [98] 2023 112 PFS Retrospective LifeX (version 5.1) DLBCLStatisticalanalysisRad-Sig ((histo kurtosis, histo energy,shape sphericity, and neighboring graylevel dependence matrix contrast),significantly associated with themetabolic GEP–based signature (r = 0.43,p = 0.0027)PFS: p = 0.028A mitochondrial metabolism-relatedgene expression pattern correlateswith specific radiomic features andpatient outcomes.Combining FDG-PET radiomics withMTV enhances the accuracy ofPET-based prognosis.Ferrer-Loreset al. [99] 2023 33Response toTherapy RetrospectiveTexture analysisQuibim DLBCLStatisticalanalysisCombine model:AUC = 0.904, accuracy = 90%Predictive model (manual):GLSZM_GrayLevel, Variance: p = 0.048,Sphericity: p = 0.027, GLCM_Correlation:p = 0.05, BCL6 amplification = p = 0.018A successful prediction of theresponse to initial treatment wasachieved by combining imagingcharacteristics, clinical factors, andgenomic data.GLSZM (GrayLevelVariance),Sphericity and GLCM(Correlation)were predictors of response.The amplification of BCL6 emergedas a highly predictive genetic marker.Cancers 2024, 16, 3511 20 of 38Table 1. Cont.Study Year P.N Task Study Design Feature ExtractionSoftware Type Classifier Results (Based on Best Model) Key FindingsZhouet al. [101] 2022 24predict survivaloutcomes (usingradiomics andgenomics data)Retrospective LifeX(version 6.30) B-CellStatisticalanalysisPFS:NGLDM: HR = 15.16, p = 0.023MYC and BCL2 double-expressor (DE):HR = 7.02, p = 0.047The integration of NGLDM_ContrastPETand DE:Group 1 (risk factors = 0; Number ofpatients: 7): PFS: 85.7% and OS: 100%Group 2 (risk factor = 1; Number ofpatients: 11): PFS: 63.6% and OS: 90.9%Group3 (risk factors = 3; Number ofpatients: 6): PFS: 0% and OS: 16.7%Combining 18F-FDG PET/CTradiomic features with genomicfactors has the potential to forecastthe survival outcomes of B-Cellpatients undergoing CART-cell therapy.there was no noteworthy correlationfound between PET/CT variablesand CRS.Triumbariet al. [110] 2023 227 PFS, DS Retrospective Moddicom cHLStatisticalanalysisPFS (mainly depended on hottest lesion):AUC: 0.74DS (mainly depended on largest lesion):AUC: 0.78Radiomics models that focus on thelargest and most active lesions offervaluable insights into the prognosisof patients.Zhou,et al. [87] 2023 61 PFS, OS RetrospectiveLifeX(version 6.30) DLBCLStatisticalanalysisPFS: C-Index = 0.710 vs. 0.640 invalidation, AUC = 0.776 vs. 0.886OS: C-Index = 0.780 vs. 0.676,AUC = 0.828 vs. 0.778The radiomics model consistentlyoutperforms in predicting both PFSand OS, not only in the main analysisbut also during validation,highlighting its superiorperformance.Albano,et al. [111] 2024 137Detecting Richtertransformation(RT), OSRetrospective LifeX(version 6.30)Chroniclymphocyticleukemia(CLL)StatisticalanalysisRichter Transformation (RT): In 130 out of137 (95%) PET/CT scans, increased traceruptake was observed. SUVbw, SUVlbm,SUVbsa, L-L SUV ratio, and L-BP SUVratio were significantly higher in the RTgroup (p < 0.001).OS: Median OS was 28 months forpatients with RT and 34 months for thosewithout RT (p = 0.002).Higher SUV metrics and L-BP SUVratio were linked to worse outcomesin RT, with a median OS of 28 monthsversus 34 months without RT.Cancers 2024, 16, 3511 21 of 38Table 1. Cont.Study Year P.N Task Study Design Feature ExtractionSoftware Type Classifier Results (Based on Best Model) Key FindingsYousefirizi,et al. [112] 2024 31Relapse/Progressionand TTPPredictionRetrospective PyradiomicsPrimarymediastinallarge B-celllymphomaMLRelapse/Progression (Delta radiomics):Accuracy: 0.89 ± 0.03F1 Score: 0.89 ± 0.03Best Model (TTP):C-index = 0.68 ± 0.09 (EoT radiomics)Delta radiomics significantlyimproved the prediction ofrelapse/progression compared toEoT radiomics.Combining baseline and Deltaradiomic features was as effective asusing EoT radiomics alone forpredicting TTP in PMBCL patients.Travainiet al. [108] 2024 112 PFS, OS RetrospectiveLifeX (version6.32) DLBCLStatisticalAnlysisPFS: C-Index = 0.84 (0.77–0.91)OS: C-Index = 0.90 (0.81–0.98)The combined clinical–radiomicmodel is better for predicting PFS,OS in DLBCL patients compared tousing radiomics or clinicalparameters alone.Progression-free survival: PFS, overall survival: OS, negative predictive value: NPV, positive predictive value: PPV, 2-year event-free survival: 2-EFS, hazard ratio: HR, radiomics Score:RS, primary treatment failure: PTF, 2-year time to progression: 2Y-TTP, gene expression profiling: GEP, cytokine release syndrome: CRS, run length non-uniformity extracted from agray level run length matrix: RLNGLRLM, gray-level non-uniformity: GLNU, area under the curve: AUC, computer-aided diagnosis: CAD, standardized uptake value: SUV, Bonemarrow involvement: BMI, mantle cell lymphoma: MCL, follicular lymphoma: FL, diffuse large b cell lymphoma: DLBCL, bone marrow uptake: BMU machine learning, deep learning,radiomics signature: Rad-Sig, gray-level co-occurrence matrix: GLCM, neighboring gray-level dependence matrix: NGLDM, End of Therapy: EoT, cell-of-origin: COO, High-intensityrun emphasis: HIR, run-length nonuniformity: RLNU, gray-level run-length matrix: GLRM, intensity nonuniformity: INU, wavelet short run emphasis: SRE, zone-size nonuniformity:ZSNU, gray-level size zone matrix: GLSZM, odd ratio: OR, total metabolic tumor volume: TMTV, metabolic bulk volume: MBV, extranodal natural killer (NK)/T cell lymphoma:ENKTL, classical hodgkin lymphomas: cHL, international prognostic index: IPI, tumor-to-liver ratio: TLR, Deauville score at interim PET/CT: DS, refractory/relapse: R/R, concordanceindex: C-index, patients’ number: P.N, primary gastrointestinal-DLBCL: pGI-DLBCL, cause-specific survival: CSS, the maximum standardized uptake value body weight: SUVbw, leanbody mass: SUVlbm, body surface area: SUVbsa, lesion-to-blood-pool SUV ratio: L-BP SUV R, lesion-to-liver SUV ratio: L-L SUV R.Cancers 2024, 16, 3511 22 of 384.4. AI-Driven Lymphoma Diagnosis and Prognosis Tool: Synergizing AI and PET/CTThese days, PET/CT imaging is essential to the treatment of lymphoma patients [5].However, because affected nodes, organs, and physiological uptake patterns vary, identi-fying and accurately demarcating lymphoma lesions on whole-body FDG-PET/CT scanspresents considerable challenges [113]. Accurate segmentation in PET/CT images is vitalfor radiotherapy treatment planning [114] and prognostic evaluation. Parameters, such asTotal MTV (TMTV) and total lesion glycolysis (TLG), extracted from segmented images,serve as valuable prognostic markers [115–117]. Unfortunately, due to the limitations ofdata in this type of imaging, performing this task manually has several drawbacks. Theinherent challenges of this imaging technique make it extremely time-consuming and sus-ceptible to human error [69]. Artificial intelligence (AI) is developing quickly and has a lotof potential to help physicians diagnose and treat cancer patients [118]. We will explore thepossibility of combining AI and PET/CT for better lymphoma treatment in the next section.4.4.1. Imaging-Based Biomarkers PredictionIn recent years, MTV has proven to be a valuable prognostic imaging-based biomarkerin lymphoma. Additionally, in DLBCL patients, TMTV demonstrated notable prognosticsignificance in predicting both PFS and OS (Figure 7) [119–121]. However, current manualmethods for measuring MTV are time-consuming, emphasizing the necessity of developingan automated MTV calculation method with the assistance of deep learning [122,123].In a recent study, it was found that AI-driven software for detecting lesions providesautomated, rapid, dependable, and consistently effective solutions for regularly acquiringmeasurements of TMTV and TLG [124]. Measuring Dmax, the largest distance betweenlymphoma sites, as a robust independent prognostic factor in DLBCL patients highlightsits potential clinical significance.This research raises the possibility of using this measure in deep learning-guidedsegmentation techniques for clinical evaluation [125]. Prognostic biomarkers, such asTMTV [126–128], Dmax [127], and MTV [122,129], can be automatically segmented andestimated in DLBCL patients by AI-based algorithms, more specifically, CNN-based algo-rithms. For DLBCL patients, these techniques can produce quantitative data in the form ofa predictive TMTV for prognosis prediction [129]. Figure 8 shows a comparison betweenthe performance of a human reader and CNN-based automatic TMTV calculation.In comparison with conventional deep learning (CDL), weakly supervised deep learn-ing (WSDL) demonstrated higher effectiveness in extracting 18F-FDG PET/CT features andpredicting the prognosis in ENKTL patients. This superiority is attributed to the utilizationof a larger training dataset and the capability to refine unclean data. WSDL and CDLachieved notable prediction sensitivities of 87.50% and 62.50%, specificities of 83.33% and83.33%, and accuracies of 85.00% and 75.00%, respectively [81]. To establish it as a robustclinical tool, potentially providing confidence levels for risk classification, future researchshould focus on validating this CNN-based approach in larger multicenter cohorts.Jiang et al. conducted a study using deep learning to develop PET-based imagingbiomarkers for precise survival prediction in DLBCL patients. The results demonstrated theefficacy of these biomarkers in predicting both PFS and OS. Furthermore, multiparametricmodels that incorporated these biomarkers surpassed other models in both the training (PFS:C-index = 0.866 and OS: C-index = 0.835) and external validation cohorts (PFS: C-index = 0.760and OS: C-index = 0.770), underscoring their potential for stratifying patient risk [130].Lymphoma segmentation is crucial in clinical diagnosis because it involves accurateidentification and the outlining of lymphoma lesions in medical images, such as 18F-FDGPET/CT scans. This precision is essential for determining the extent of the disease, planningeffective treatments, monitoring how tumors respond to therapy, and extracting detailedradiomic features that aid in prognosis. Effective segmentation ensures that clinicians havethe necessary information to make informed decisions, leading to more personalized andsuccessful patient care. It is especially important for distinguishing between malignant andbenign lymphomas and is essential for treatment planning. In this context, several studiesCancers 2024, 16, 3511 23 of 38have developed innovative DL models to enhance the precision and reliability of delineatinglymphoma boundaries in PET images (Dice coefficients of 87.18% and 0.8796) [131,132].Similarly, a different study presented another proposed deep learning-based semi-automatedlymphoma segmentation approach (Dice coefficient = 0.907) [133]. Additionally, a separatestudy introduced a computer-aided diagnosis (CAD) system to accurately segment ENKTlymphoma lesions (Dice coefficient = 0.7115, sensitivity = 0.7472) [134]. A recent study hasintroduced an unsupervised image generation approach that leverages anatomical-metabolicconsistency representations. This method significantly improves the accuracy of lymphomasegmentation in PET/CT images, demonstrating substantial potential for practical clinicalapplications and aiding clinical diagnosis [75].Eertink et al. reported that the utilization of various data segmentation techniquesresulted in differences in radiomics feature values and the selection of specific featuresfor DLBCL [135]. Nonetheless, disparities can be found in the actual values of radiomicfeatures acquired and the selected features when utilizing various segmentation techniques.Significant correlations were identified among MTV, intensity, and various disseminationfeatures derived from different segmentation techniques, indicating a persistent and reliableprognostic performance. Although a threshold-based segmentation using an SUV levelof 4.0 offers practical advantages in cHL PET/CT segmentation, its effectiveness withsmaller, less avid lesions requires further evaluation in a larger patient cohort to confirm itsclinical applicability [136]. A multi-resolution 3D U-Net model for TMTV segmentation inlymphoma achieved an average Dice score of 0.68 on internal data and 0.66 on multi-centerexternal data, with a correlation of 0.89 to the ground truth TMTV [137].Cancers 2024, 16, x FOR PEER REVIEW 26 of 42 Figure 7. Kaplan–Meier survival plots representing PFS in panels (A,B), and OS in panels (C,D). Reprinted with permission from [119] under a Creative Commons Attribution 4.0 International Li-cense. Lymphoma segmentation is crucial in clinical diagnosis because it involves accurate identification and the outlining of lymphoma lesions in medical images, such as 18F-FDG PET/CT scans. This precision is essential for determining the extent of the disease, plan-ning effective treatments, monitoring how tumors respond to therapy, and extracting de-tailed radiomic features that aid in prognosis. Effective segmentation ensures that clini-cians have the necessary information to make informed decisions, leading to more per-sonalized and successful patient care. It is especially important for distinguishing between malignant and benign lymphomas and is essential for treatment planning. In this context, several studies have developed innovative DL models to enhance the precision and relia-bility of delineating lymphoma boundaries in PET images (Dice coefficients of 87.18% and 0.8796) [131,132]. Similarly, a different study presented another proposed deep learning-based semi-automated lymphoma segmentation approach (Dice coefficient = 0.907) [133]. Additionally, a separate study introduced a computer-aided diagnosis (CAD) system to accurately segment ENKT lymphoma lesions (Dice coefficient = 0.7115, sensitivity = 0.7472) [134]. A recent study has introduced an unsupervised image generation approach that leverages anatomical-metabolic consistency representations. This method signifi-cantly improves the accuracy of lymphoma segmentation in PET/CT images, demonstrat-ing substantial potential for practical clinical applications and aiding clinical diagnosis [75]. Figure 7. Kaplan–Meier survival plots representing PFS in panels (A,B), and OS in panels (C,D).Reprinted with permission from [119] under a Creative Commons Attribution 4.0 International License.Cancers 2024, 16, 3511 24 of 38Cancers 2024, 16, x FOR PEER REVIEW 27 of 42 Figure 8. (Top) panel: Identification and categorization of regions with elevated 18F-FDG absorption as either physiological or anomalous. (A,D) Maximum-intensity-projection PET visuals of subjects with low TMTV (A) and high TMTV (D). (B,E) ROIPARS generated automatically via the PARS soft-ware prototype. ROIPARS regions pinpointed by the MFS algorithm are superimposed on the PET maximum-intensity projection. Physiologically classified ROIPARS sites by the deep-learning mecha-nism are depicted in green, while those deemed suspicious are in yellow. (C,F) ROIREF delineated by a seasoned nuclear medicine expert utilizing semi-automatic tools. (Bottom) panel: Receiver-oper-ating-characteristic curves for TMTVPARS and TMTVREF pertaining to 4-year PFS (A) and 4-year OS (B). Corresponding AUC values and ideal TMTV thresholds are indicated. Modified and reprinted from [119] under a Creative Commons Attribution 4.0 International License. Eertink et al. reported that the utilization of various data segmentation techniques resulted in differences in radiomics feature values and the selection of specific features for Figure 8. (Top) panel: Identification and categorization of regions ith elevated 18F-FDG absorptionas either physiological or anomalous. (A,D) Maximum-intensity-projection PET visuals of subjectswith low TMTV (A) and high TMTV (D). (B,E) ROIPARS generated automatically via the PARSsoftware prototype. ROIPARS regions pinpointed by the MFS algorithm are superimposed on thePET maximum-intensity projection. Physiologically classified ROIPARS sites by the deep-learningmechanism are depicted in green, while those deemed suspicious are in yellow. (C,F) ROIREFdelineated by a seasoned nuclear medicine expert utilizing semi-automatic tools. (Bottom) panel:Receiver-o erati -characteristic curv s for TMTVPARS and TMTVREF pertaining to 4-year PFS (A)and 4-year OS (B). orresponding AUC values a d ideal TMTV thresholds are indicated. Modifiedand reprinted from [119] under a Creative Commons Attribution 4.0 International License.In the medical domain, CNN’s reliance on precise pixel-level annotations poses chal-lenges in terms of time and expertise needed for accurate medical image analysis. ToCancers 2024, 16, 3511 25 of 38address this, researchers have introduced an innovative WSD model utilizing weak la-bels and multi-scale feature similarity in PET/CT images [69]. Future work should aimto enhance the performance of the algorithms dedicated to weak and/or noisy datasets,optimize computational efficiency, expand dataset diversity for validation, and investigatereal-world clinical application feasibility.4.4.2. System-Aided Diagnosis and StagingThe utilization of PET/CT scans holds a pivotal position in the initial staging, deter-mining the most appropriate therapy. It excels in identifying lymphoma involvement inareas that might be overlooked by CT scans alone. This capability is essential in avoidingthe risk of undertreating patients with advanced disease stages who could otherwise be mis-takenly classified as having limited-stage disease solely based on CT findings. In essence,FDG-PET/CT significantly enhances our ability to make precise treatment decisions inlymphoma cases [5].The Ann Arbor classification continues to serve as the gold standard staging systemfor non-cutaneous lymphomas, categorizing lymphoma patients into four distinct diseasestages. In accordance with the Lugano classification, FDG-PET/CT is now routinelyrecommended for all FDG-avid lymphomas, representing a professional consensus inmodern lymphoma staging and diagnosis [5,138]. The Deauville 5-point scale can alsobe considered a treatment response evaluation system in lymphoma patients based onPET/CT images. This system can also lead to variations in assessments among differentobservers. To address this challenge, Sadik et al. introduced an AI-driven method toquantify reference levels in both the liver and the mediastinal blood pool [139]. In thevalidation group, AI-based measurements demonstrated good agreement with radiologists(in the validation group, the mean difference for quantifying reference levels was 0.02in both the liver and the mediastinal blood pool). One key study showed that domainadaptation significantly enhanced language models’ ability to predict five-point Deauvillescores from PET/CT reports [140]. Deep learning effectively differentiates 18F-FDG-PET-CTscans of lymphoma patients with and without hypermetabolic tumor sites, suggestingits potential as a tool for ruling out metabolically active disease or as a second reader ina clinical setting (achieving an AUC of 0.953, accuracy of 0.907, sensitivity of 0.874, andspecificity of 0.949 in external testing) [141].In DLBCL patients, deep learning scores from PET images provide valuable survivalprognostication, with multiparametric models achieving C-indices of 0.866 for PFS and0.835 for OS in training, and 0.760 and 0.770 for PFS and 0.748 and 0.766 for OS in externalvalidation [130]. Recent research found that deep learning-enhanced PET imaging im-proved image quality and diagnostic accuracy while reducing imaging duration by 50% forlymphoma patients [142]. The hybrid few-shot multiple-instance learning model achieved0.75 accuracy and 0.795 AUC in predicting lymphoma aggressiveness [143].As mentioned at the beginning of this section, lymphoma detection can be prone tomisinterpretation by physicians due to the inherent limitations of this type of imaging.The integration of artificial intelligence-driven automatic detection and segmentation algo-rithms has the potential to significantly enhance the efficiency and accuracy of physiciansin analyzing these images [69].In this regard, Yu et al. introduced a novel semi-automatic graphical model-basedapproach for both detection, achieving a 100% detection rate, and segmentation, with aDice coefficient of 84.4% [144]. 18F-FDG PET/CT scans offer valuable prognostic insightsfor individuals with MCL [145]. However, manual detection of MCL lesions on these scansis labor-intensive and not currently integrated into routine clinical procedures. DL-basedmodels show promising levels of accuracy in detecting MCL on PET/CT images, suggestingpotential wider clinical applications. In another study, a substantial enhancement in theclassification accuracy of areas showing normal physiological FDG uptake and excretionin lymphoma PET/CT scans was observed (F1-score of 91.73%). This improved precisionCancers 2024, 16, 3511 26 of 38can aid in early detection, improved treatment, and prognostic assessments for lymphomapatients, all while minimizing false positives during image interpretation [146].Given the growing use of chatbots for everyday problem-solving, researchers recentlyevaluated if an AI-based language model, specifically ChatGPT, developed by OpenAI, caneffectively answer patient questions about 18F-FDG PET/CT scans. As shown in (Figure 9),the study involved thirteen questions and six PET/CT reports (lung cancer, Hodgkinlymphoma). Nuclear medicine staff rated the responses for appropriateness and usefulness.Results showed ChatGPT provided “appropriate” responses for 92% and “useful” answersfor 96% of the questions; however, 16% of responses were inconsistent [147].Cancers 2024, 16, x FOR PEER REVIEW 29 of 42 The integration of artificial intelligence-driven automatic detection and segmentation al-gorithms has the potential to significantly enhance the efficiency and accuracy of physi-cians in analyzing these images [69]. In this regard, Yu et al. introduced a novel semi-automatic graphical model-based approach for both detection, achieving a 100% detection rate, and segmentation, with a Dice coefficient of 84.4% [144]. 18F-FDG PET/CT scans offer valuable prognostic insights for individuals with MCL [145]. However, manual detection of MCL lesions on these scans is labor-intensive and not currently integrated into routine clinical procedures. DL-based models show promising levels of accuracy in detecting MCL on PET/CT images, suggest-ing potential wider clinical applications. In another study, a substantial enhancement in the classification accuracy of areas showing normal physiological FDG uptake and excre-tion in lymphoma PET/CT scans was observed (F1-score of 91.73%). This improved preci-sion can aid in early detectio , improved treatment, and prognostic assessments for lym-phoma patients, all while minimizing false positives during image interpretation [146]. Given the growing use of chatbots for everyday problem-solving, researchers recently evaluated if an AI-based language model, specifically ChatGPT, developed by OpenAI, can effectively answer patient questions about 18F-FDG PET/CT scans. As shown in (Figure 9), the study involved thirteen questions and six PET/CT reports (lung cancer, Hodgkin lym-phoma). Nuclear medicine staff rated the responses for appropriateness and usefulness. Re-sults showed ChatGPT provided “appropriate” responses for 92% and “useful” answers for 96% of the questions; however, 16% of responses were inconsistent [147]. Figure 9. The study with thirteen questions and six PET/CT reports found ChatGPTʹs responses “appropriate” 92% and “useful” 96% of the time, though 16% were inconsistent. This work was originally published in JNM [147]. In a recent study, Constantino et al. found that the Self-Adaptive Configuration, SAC (Bayesian method outperformed others in robustness, reproducibility, and accuracy on [18F]FDG PET/CT lymphoma images, achieving the highest intra-observer Dice Coefficient (0.87) and inter-observer DC (0.94), surpassed manual segmentation in tumor quantifica-tion [148]. It can replace manual segmentation for lymphoma lesions, ensuring consistent quantitative measurements and facilitating the creation of accurate, large datasets for deep learning-based segmentation models. The study advises against using absolute or relative threshold-based methods in clinical studies due to potential substantial differ-ences in important lesion features, such as SUVmean, MTV, and TLG, compared to manual segmentation. For future research, it is recommended to conduct tests on a larger number of patients, explore the combination of PET and CT images to enhance unary energy using graph-based models, and develop a more comprehensive multi-atlas to reduce false pos-itives. Figure 9. The study with thirteen questions and six PET/CT reports found ChatGPT’s responses“appropriate” 92% and “useful” 96% of the time, though 16% were inconsistent. This work wasoriginally published in JNM [147].In a recent study, Constantino et al. found that the Self-Adaptive Configuration, SAC(Bayesian method outperformed others in robustness, reproducibility, and accuracy on[18F]FDG PET/CT lymphoma images, achieving the highest intra-observer Dice Coefficient(0.87) and inter-observer DC (0.94), surpassed manual segmentation in tumor quantifica-tion [148]. It can replace manual segmentation for lymphoma lesions, ensuring consistentquantitative measurements and facilitating the creation of accurate, large datasets for deeplearning-based segmentation models. The study advises against using absolute or relativethreshold-based methods in clinical studies due to potential substantial differences inimportant lesion features, such s SUVmean, MTV, and TLG, comp red to m ual segmenta-tion. For future research, it is recommended to conduct t sts on a larger number of patie ts,explore the combin io of PET and CT images to enhance unary energy u ing graph-basedmodels, and develop a more c mprehensive multi-atlas to reduce fal e positives.Cancers 2024, 16, 3511 27 of 38Table 2. Summary of AI-assisted studies conducted on lymphoma using 18F-FDG PET/CT images. All studies in this table are retrospective, except for Blanc-Durandet al. [126], which is prospective. This difference emphasizes a key limitation in the research field, as retrospective studies may introduce biases that could impact theinterpretation of the results.Study Year P.N Task Study Design Subtype Algorithms Results Clinical Applications Key FindingsCapobiancoet al. [119] 2021 301Automaticcalculation ofMTVClassification ofhigh-uptake regionsPFS, OSRetrospective DLBCLCNN (classification),Survival: statisticalanalysisCorrelation between TMTVPARS andTMTVREF: (ρ = 0.76; p < 0.001).The classification accuracy was 85%.Sensitivity was 80%.Specificity was 88%.PFS:HR for TMTVPARS: 2.3 (p < 0.001).HR for TMTVREF: 2.6 (p < 0.001).OS:HR for TMTVPARS: 2.8 (p < 0.001).HR for TMTVREF: 3.7 (p < 0.001).Simplified TMTV estimation,reduced observer Variability,provide valuable prognosticinformation to improve patientcare and decision-making.DL-based automation canestimate TMTV and underscoresits significance as a valuableprognostic tool.The resulting TMTV, obtainedthrough this automated DL,demonstrated significantprognostic value for bothPFS, OS.Pinochetet al. [120] 2021 119Automaticcalculation ofMTVRetrospective DLBCL DCNNmedian DSC score: 0.65, ICC betweenautomatically and manually obtainedTMTVs: 0.68.PFS (automatically based TMTV vs.manually based TMTV): HR = 2.1 vs. 3.3OS: HR = 2.4 vs. 3.1Enhance cancer detection,provide valuable prognosticinformation, tailor treatmentplans, and support clinicaldecision-makingPerformance and predictivevalue of both automatic andmanual TMTV measurements forPFS and OS in the respectivepatient cohorts.Huanget al. [69] 2020 147AutomaticSegmentation Retrospective DLBCL WSDL DSC = 71.47%.Reduced dependence onexpert annotationsImproving the diagnosis,treatment planning, andmonitoring of DLBCLTheir method accuratelysegmented lymphoma inPET/CT images using weaklabels, reducing the need forexpert annotations.Kukeret al. [122] 2022 100Automaticcalculation ofMTVRetrospective DLBCL DCNNReader 1 vs. AM (Automated Method):Pearson’s Correlation Coefficient: 0.9814(p < 0.0001)ICC: 0.98 (p < 0.001)Reader 2 vs. AM (Automated Method):Pearson’s Correlation Coefficient: 0.9818(p < 0.0001)ICC: 0.98 (p < 0.0001)Automated MTV CalculationEnhances the reliability ofprognostic biomarkers, and hasimplications fortreatment planningThe automated MTV calculationclosely matches nuclear medicinereader measurements, offeringefficiency and reproducibilitywithout extensive training data,suitable for clinical research.Karimdjeeet al. [124] 2023 51Automaticcalculation ofMTVRetrospective DLBCLMethods 1 and 2: fullyautomated withexclusion of lesions≤0.5 mL and≤0.1 mL, respectively.Methods 3 and 4: fullyautomated withphysician review.Method 5:semi-automatedFor the main user between methods 3 and 5:ICC for TMTV: 0.99, ICC for TLG: 1.0Between the two users applying method 3:ICC for TMTV: 0.97, ICC for TLG: 0.99Mean processing time (±standard deviation):Method 1: 20 s ± 9.0Method 3: 178 s ± 125.7Method 5: 326 s ± 188.6 (p < 0.05)Enhances the precision andefficiency of cancer diagnosisand treatment monitoring in aclinical workflow.AI-based lesion detectionsoftware is a reliable tool fordaily TMTV andTLG measurements.Cancers 2024, 16, 3511 28 of 38Table 2. Cont.Study Year P.N Task Study Design Subtype Algorithms Results Clinical Applications Key FindingsBlanc-Durandet al. [126] 733Automaticcalculation ofTMTVProspective DLBCL CNNValidation Set Results:DSC: 0.73 ± 0.20Mean Jaccard Coefficient: 0.68 ± 0.21Underestimation of mean TMTV: −116 mL(20.8%) ± 425 (statistically significant,p = 0.01)Training (n = 693):Underestimation of mean TMTV: −12 mL(2.8%) ± 263 (not statistically significant,p = 0.27)Accurate tumor volumeassessment.helps oncologists determine theoptimal treatment strategy.diagnostic assistance, increasingthe reproducibility of TMTVassessmentCNN holds promise forautomating the detection andsegmentation oflymphoma lesions.Girumet al. [127] 2022 382Automaticcalculation ofMTV and Dmaxusing 2MIPsRetrospective DLBCL CNNCorrelation coefficients:sTMTV correlation with TMTV for center 1:Spearman r = 0.878sTMTV correlation with TMTV for center 2:Spearman r = 0.752sDmax correlation with Dmax: r = 0.709Dmax correlation with Dmax: r = 0.714Hazard Ratios for PFS (95% CI):TMTV: 11.24, sTMTV: 11.81, Dmax: 9.0,sDmax: 12.49Uniformity in BiomarkerComputation.Calculation of biomarkers.Marked Improvement inDiagnostic and PrognosticEfficiencySimplify the computation andapplication of thesecharacteristics in clinical settingsAI algorithms can automaticallyestimate surrogate TMTV andDmax, which are prognosticbiomarkers, using only 2 PETMIP images.Zhouet al. [145] 2021 142 Detection Retrospective MCL DLCNNCenter1: sensitivity of 88% (IQR: 25%) with15 (IQR: 12) FPs/patientcenter 2: sensitivity of 84% (IQR: 24%) with14 (IQR: 10) FPs/patientImproved diagnostic accuracy,reduced labor-intensive analysis,early detection and stagingDLCNN demonstrated highsensitivity for the detectionof MLCPET-assisted reporting system: PARS, deep convolutional neural network: DCNN, surrogate TMTV: sTMTV, surrogate Dmax: sDmax, metabolic tumor volume: TMTV, total lesionglycolysis: TLG, intra-class coefficient: ICC, weakly supervised deep learning: WSDL, dice similarity coefficient: DSC, deep learning: DL, progression-free survival: PFS, time-to-progression: TTP, overall survival: OS, diffuse large b cell lymphoma: DLBCL, maximum tumor dissemnination: Dmax, mantle cell lymphoma: MCL, interquartile range: IQR, falsepositives: FPs, maximum intensity projections: MIPs, patients number: P.N.Cancers 2024, 16, 3511 29 of 385. Conclusions and Future DirectionsThis review provides an up-to-date overview of the utilization of PET/CT radiomicfeatures and artificial intelligence in the management of lymphoma patients, highlightingtheir significant implications for precision medicine and improved patient care. However,further investigation is needed before these methods can be incorporated into routineclinical practice.Radiomic features derived from 18F-FDG PET/CT scans have the potential to provideinsights into bone marrow involvement (BMI) status in lymphoma patients [25,28–31,96].Studies have shown that combining radiomic features with clinical data enhances diagnos-tic accuracy, with area under the curve (AUC) values ranging from 0.68 to 0.822, sensitivityup to 81.8%, and specificity up to 83.3%. These results indicate from moderate to higheffectiveness in detecting BMI, surpassing traditional diagnostic methods like bone marrowbiopsy (BMB). However, the limited number of studies and the lack of multi-institutionallarge-scale research prevent us from reaching a definitive conclusion. Utilizing a multi-faceted approach that integrates clinical, biological, and PET features could improve thediagnosis of BMI in lymphoma patients. Extensive prospective clinical studies with largersample sizes are necessary to assess the practicality of 18F-FDG PET/CT radiomics in aclinical setting.While some studies have highlighted the potential of radiomic features and machinelearning (ML)-based radiomics models in differentiating various tumor types [39–48], thereis a limited number of studies focusing on distinguishing the histological subtypes oflymphoma [35–37]. Radiomics and ML models have effectively distinguished betweendifferent lymphoma subtypes, achieving AUCs as high as 0.95 and accuracies up to 86.96%,outperforming conventional metrics, such as SUVmax [35–37]. Additionally, radiomics maydifferentiate between lymph node metastasis and lymphoma involvement [38]. Futurestudies should be conducted on larger datasets, different lymphoma subtypes, and lymphnode metastasis from primary lesions. There is hope that future research can positionradiomics as an additional non-invasive tool to further investigate the characteristicsof lymphoma, potentially reducing the need for invasive biopsies and leading to morepersonalized treatment strategies.Most research utilizes radiomics or AI-based PET/CT radiomics models to predictprognosis or outcomes in various lymphoma subtypes, particularly diffuse large B-celllymphoma (DLBCL). As seen in Table 1, a significant number of radiomics studies focus onDLBCL in outcome prediction and response to treatment [18,56,59,61–63,68,69,71,72,76,77,80,82–84,87,98,105,112,117,149–151]. These studies reported high accuracies of up to 90% andAUCs of up to 0.952, indicating that radiomics-based predictive models, especially whenintegrated with clinical data, can serve as valuable prognostic tools. The findings demonstratethat radiomic models often outperform traditional methods in diagnostic accuracy, treatmentresponse prediction, and survival outcome forecasting. However, further research in this areais warranted to validate these findings across different lymphoma subtypes.As discussed above, promising research has primarily focused on predicting treatmentresponse and survival using radiomics. Despite the significance of integrating radiomicsand genomics, there has been very limited research in the context of lymphoma. Radio-genomics offers a pathway to enhance our understanding of the molecular mechanismsunderlying tumor development. Moreover, it provides valuable insights for identifyingunique traits in cancer patients, facilitating clinical decision-making by predicting prog-nosis and advancing the development of personalized treatment strategies. Therefore, itis imperative that future research prioritizes the acquisition of knowledge through theintegration of radiomics and genetic data to enhance lymphoma management.Liquid biopsy (LB) and radiomics offer promising characteristics in the field of cancerresearch, as they are both quantifiable and non-invasive and have the potential to berepeated during patient monitoring [107]. However, there is a significant research gap inthe untapped potential for synergy between liquid biopsy and radiomic features extractedfrom PET images. The exciting opportunity lies in combining the genetic insights fromCancers 2024, 16, 3511 30 of 38liquid biopsy with the quantitative data from PET images using radiomics. This synergyhas the potential to enhance our understanding of lymphoma and can lead to more accuratediagnoses and tailored treatment strategies.Furthermore, in this review, we have emphasized the significance of AI-based toolsas valuable aids for medical practitioners. Over recent years, there has been a growinginterest in utilizing AI to assist physicians in addressing challenges associated with theinterpretation of PET/CT images. This emerging field tackles various image-related chal-lenges, including low resolution, limited contrast, unclear boundaries, variations in tissueabsorption, and individual differences. The application of AI models has proven essentialfor a wide range of tasks, including tumor diagnosis, image segmentation, post-treatmentevaluation, pathology prediction, and estimation of survival rates.In addition, AI holds great promise in enhancing the quality of 18F-FDG PET imag-ing [152], particularly in cases where structural imaging is unavailable. Despite thesepromising developments, several formidable hurdles must be overcome to practicallyimplement AI-based physician assistant tools in clinical settings.Radiomics has the potential to greatly improve lymphoma patient care by providingmore accurate diagnoses, personalized treatment plans, and better outcome predictions. Byestablishing standards for data collection and feature extraction, AI models can becomemore accurate and stable, allowing clinicians to predict how patients will respond to treat-ments, such as chemotherapy or radiotherapy. Analyzing baseline images can identifypatients at higher risk of relapse, enabling early adjustments in treatment strategies. Feder-ated learning can further enhance AI models by training on large, diverse datasets withoutsharing sensitive patient data, thus improving prediction accuracy and applicability. Futureclinical studies should focus on integrating AI-driven decision support tools into diagnosisand treatment, helping physicians offer more precise, personalized care while reducing theneed for invasive procedures.As highlighted by Hasani et al. [15], AI holds great potential in lymphoma PET imag-ing, particularly in tasks such as lesion detection, classification, segmentation, and predict-ing treatment outcomes. Manual delineation of hypermetabolic lesions is time-consuming,but AI can automate these tasks and improve tumor characterization. Implementing AI-based tools in clinical settings requires large, diverse datasets and multi-center validationto ensure robustness and generalizability.For future research, advanced models, such as Vision Transformers (ViT) and Attention-based CNNs, can enhance quantitative image analysis, while a Hybrid AI–Human Collabora-tion Model can combine AI automation with human expertise. Federated learning also offersa practical solution for training models across institutions without sharing sensitive data.Addressing these challenges will enable more robust AI applications in lymphoma care.As indicated in Tables 1 and 2, the majority of studies included in this analysis (upto 95%) are retrospective in design. While retrospective studies are valuable, they canintroduce certain limitations, such as selection and recall bias, which may influence theresults. Therefore, these limitations should be carefully considered when interpreting thefindings of this research.In conclusion, the comparative analysis of current studies underscores the significantpotential of radiomics and AI in improving lymphoma management. While many studiesfocused on DLBCL for outcome prediction and treatment response, there is a need forfurther research on other lymphoma subtypes, especially in the context of BMI diagnosisand histologic differentiation. The numerical results from these studies indicate thatradiomic models often outperform traditional methods. However, the variability in studydesigns, lymphoma subtypes, and methodologies highlights the necessity for standardizedapproaches and larger, multi-center studies to validate these findings. We recognize thatvariations in reported performance metrics, including specificity, sensitivity, and areaunder the curve (AUC), pose significant challenges in directly comparing the predictiveperformance of different studies. By addressing these challenges, radiomics and AI can beCancers 2024, 16, 3511 31 of 38more effectively integrated into routine clinical practice, ultimately enhancing patient careand outcomes in lymphoma.Author Contributions: Conceptualization, S.H., S.M.R.A., A.A.A. and H.A. (Hossein Arabi); methodol-ogy, S.H., A.A.A. and H.A. (Hossein Arabi); software, S.H.; validation, S.H., S.M.R.A., A.A.A. and H.A.(Hossein Arabi); formal analysis, S.H.; investigation, S.H., S.M.R.A., A.A.A., H.A. (Hamid Abdollahi)and H.A. (Hossein Arabi); resources, S.H. and H.A. (Hossein Arabi); data curation, S.H.; writing—originaldraft preparation, S.H.; writing—review and editing, S.H., S.M.R.A., A.A.A., H.A. (Hossein Arabi) andH.Z.; visualization, S.H.; supervision, S.M.R.A.; project administration, S.M.R.A.; funding acquisition,none. All authors have read and agreed to the published version of the manuscript.Funding: This work was supported by the Swiss National Science Foundation under grant SNSF320030-231742.Acknowledgments: We would like to express our deepest gratitude to Mehrdad Bakhshayesh Karamfrom Shahid Beheshti University of Medical Sciences for his invaluable guidance. 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Vancouver : University of British Columbia Library
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lymphoma
18F-FDG PET/CT
radiomics
genomics
radiogenomics
deep learning
personalized therapy
Enhancing Lymphoma Diagnosis, Treatment, and Follow-Up Using ¹⁸F-FDG PET/CT Imaging: Contribution of Artificial Intelligence and Radiomics Analysis
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