{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T18:17:33Z","timestamp":1777745853818,"version":"3.51.4"},"reference-count":83,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T00:00:00Z","timestamp":1777334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Weill Cornell Medicine\u2014Qatar Health Sciences Library"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Tomography"],"abstract":"<jats:p>Background\/Objectives: Prostate cancer (PCa) is the second most commonly diagnosed cancer in men globally. Radiation oncologists often find PCa tumor characterization and outcome prediction challenging. Therefore, the potential for artificial intelligence (AI) implementation in radiation oncology has increased in recent years. This systematic review aims to evaluate the efficacy of AI algorithms in characterizing PCa tumors and predicting post-therapy outcomes. Methods: A total of 2055 studies were identified through a comprehensive search across PubMed and Scopus, then exported to Covidence. Inclusion criteria focused on prospective and retrospective cohort studies as well as randomized clinical trials (RCTs) published between 2015 and 2024 that explored the implementation of AI in tumor characterization and outcome prediction of PCa. Two independent reviewers evaluated each paper, and evaluation metrics such as specificity, sensitivity, accuracy, and area under the curve (AUC) were analyzed. The Risk of Bias in Non-randomized Studies of Interventions, Version 2 (ROBINS-I V2) tool was used to assess the risk of bias (ROB). Results: Across the 19 studies analyzed, there was no significant difference in model performance between machine learning (ML) and deep learning (DL) models. AI models using multi-input strategies (e.g., radiomics with clinical markers) generally performed better than single-input models. Of the imaging modalities used for radiomic feature extraction, multiparametric MRI (mpMRI)-trained AI models consistently achieved the highest performance. Conclusions: AI displays considerable potential for integration into clinical workflows for PCa management. However, further studies utilizing larger datasets and external cohorts independent of the sample population are needed to validate clinical utility and improve model transparency for reliable implementation.<\/jats:p>","DOI":"10.3390\/tomography12050062","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T10:34:42Z","timestamp":1777458882000},"page":"62","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The Role of Artificial Intelligence in the Characterization and Outcome Prediction of Prostate Cancer: A Systematic Review"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6128-3554","authenticated-orcid":false,"given":"Shahd","family":"Aljoudi","sequence":"first","affiliation":[{"name":"Weill Cornell Medicine Qatar, Education City, Qatar Foundation, Al Luqta St. Ar-Rayyan, Doha P.O. Box 24144, Qatar"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8206-915X","authenticated-orcid":false,"given":"Aasiya","family":"Khan","sequence":"additional","affiliation":[{"name":"Weill Cornell Medicine Qatar, Education City, Qatar Foundation, Al Luqta St. Ar-Rayyan, Doha P.O. Box 24144, Qatar"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6793-4348","authenticated-orcid":false,"given":"Iman","family":"Dajani","sequence":"additional","affiliation":[{"name":"Weill Cornell Medicine Qatar, Education City, Qatar Foundation, Al Luqta St. Ar-Rayyan, Doha P.O. Box 24144, Qatar"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7448-8770","authenticated-orcid":false,"given":"Minatullah","family":"Al-Ani","sequence":"additional","affiliation":[{"name":"Weill Cornell Medicine Qatar, Education City, Qatar Foundation, Al Luqta St. Ar-Rayyan, Doha P.O. Box 24144, Qatar"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7365-953X","authenticated-orcid":false,"given":"Michael","family":"Mina","sequence":"additional","affiliation":[{"name":"Weill Cornell Medicine Qatar, Education City, Qatar Foundation, Al Luqta St. Ar-Rayyan, Doha P.O. Box 24144, Qatar"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5438-5532","authenticated-orcid":false,"given":"Dounia","family":"Baroudi","sequence":"additional","affiliation":[{"name":"Weill Cornell Medicine Qatar, Education City, Qatar Foundation, Al Luqta St. Ar-Rayyan, Doha P.O. Box 24144, Qatar"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4922-0844","authenticated-orcid":false,"given":"Sama","family":"Al-Saffar","sequence":"additional","affiliation":[{"name":"Weill Cornell Medicine Qatar, Education City, Qatar Foundation, Al Luqta St. Ar-Rayyan, Doha P.O. Box 24144, Qatar"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9839-2908","authenticated-orcid":false,"given":"Souha","family":"Aouadi","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, National Centre for Cancer Care and Research, Hamad Medical Corporation, Doha P.O. Box 3050, Qatar"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8758-9481","authenticated-orcid":false,"given":"Tarraf","family":"Torfeh","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, National Centre for Cancer Care and Research, Hamad Medical Corporation, Doha P.O. Box 3050, Qatar"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5537-3942","authenticated-orcid":false,"given":"Rabih","family":"Hammoud","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, National Centre for Cancer Care and Research, Hamad Medical Corporation, Doha P.O. Box 3050, Qatar"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Noora","family":"Al Hammadi","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, National Centre for Cancer Care and Research, Hamad Medical Corporation, Doha P.O. Box 3050, Qatar"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0492-7658","authenticated-orcid":false,"given":"Mohammad S.","family":"Yousef","sequence":"additional","affiliation":[{"name":"Weill Cornell Medicine Qatar, Education City, Qatar Foundation, Al Luqta St. Ar-Rayyan, Doha P.O. Box 24144, Qatar"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,28]]},"reference":[{"key":"ref_1","first-page":"229","article-title":"Global Cancer Statistics 2022: Global Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","first-page":"10","article-title":"Cancer Statistics, 2025","volume":"75","author":"Siegel","year":"2025","journal-title":"CA Cancer J. Clin."},{"key":"ref_3","unstructured":"Ministry of Public Health (2020). Prostate Cancer Factsheet."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.2214\/AJR.09.2579","article-title":"Challenges in Clinical Prostate Cancer: Role of Imaging","volume":"192","author":"Kelloff","year":"2009","journal-title":"AJR. Am. J. Roentgenol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1590\/s1677-5538.ibju.2016.0656","article-title":"Biochemical Recurrence after Radical Prostatectomy: What Does It Mean?","volume":"44","author":"Srougi","year":"2018","journal-title":"Int. Braz. J. Urol."},{"key":"ref_6","first-page":"146","article-title":"Radiation Therapy for Prostate Cancer","volume":"115","author":"Gay","year":"2018","journal-title":"Mo. Med."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Giunta, E.F., Annaratone, L., Bollito, E., Porpiglia, F., Cereda, M., Banna, G.L., Mosca, A., Marchi\u00f2, C., and Rescigno, P. (2021). Molecular Characterization of Prostate Cancers in the Precision Medicine Era. Cancers, 13.","DOI":"10.3390\/cancers13194771"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"63","DOI":"10.14740\/wjon1191","article-title":"Epidemiology of Prostate Cancer","volume":"10","author":"Rawla","year":"2019","journal-title":"World J. Oncol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/S0302-2838(02)00505-5","article-title":"PSA Progression Following Radical Prostatectomy and Radiation Ther-apy: New Standards in the New Millenium","volume":"43","author":"Djavan","year":"2003","journal-title":"Eur. Urol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"818065","DOI":"10.1155\/2009\/818065","article-title":"Modalities for Imaging of Prostate Cancer","volume":"2009","author":"Hou","year":"2009","journal-title":"Adv. Urol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"E123","DOI":"10.1148\/rg.210134","article-title":"Prostate Cancer Imaging: What We Already Know and What Is on the Horizon","volume":"42","author":"Horvat","year":"2022","journal-title":"RadioGraphics"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"102968","DOI":"10.1016\/j.radi.2025.102968","article-title":"Potential of Artificial Intelligence for Radiation Dose Reduction in Computed Tomography\u2014A Scoping Review","volume":"31","author":"England","year":"2025","journal-title":"Radiography"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e66622","DOI":"10.2196\/66622","article-title":"Effectiveness of AI for Enhancing Computed Tomography Image Quality and Radiation Protection in Radiology: Systematic Review and Meta-Analysis","volume":"27","author":"Zhang","year":"2025","journal-title":"J. Med. Internet Res."},{"key":"ref_14","first-page":"1","article-title":"A Review of Imaging Methods for Prostate Cancer Detection","volume":"7","author":"Sarkar","year":"2016","journal-title":"Biomed. Eng. Comput. Biol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Nair, A., Ong, W., Lee, A., Leow, N.W., Makmur, A., Ting, Y.H., Lee, Y.J., Ong, S.J., Tan, J.J.H., and Kumar, N. (2025). Enhancing Radiologist Productivity with Artificial Intelligence in Magnetic Resonance Imaging (MRI): A Narrative Review. Diagnostics, 15.","DOI":"10.3390\/diagnostics15091146"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1055\/s-0044-1779749","article-title":"Comparison of Ga-68 PSMA PET\/CT and Multiparametric MRI for Initial Detection and Staging of Prostate Cancer","volume":"23","author":"Gauthaman","year":"2024","journal-title":"World J. Nucl. Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.ejrad.2015.10.013","article-title":"Prostate Cancer on Computed Tomography: A Direct Comparison with Multi-Parametric Magnetic Resonance Imaging and Tissue Pathology","volume":"85","author":"Jia","year":"2016","journal-title":"Eur. J. Radiol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"245","DOI":"10.4103\/ijnm.ijnm_243_20","article-title":"Comparison of Multiparametric Magnetic Resonance Imaging and Gallium-68 Prostate-Specific Membrane Antigen Positron Emission Tomography\/Computed Tomography for De-tecting Carcinoma Prostate in Patients with Serum Prostate-Specific Antigen between 4 and 20 Ng\/ML","volume":"36","author":"Soni","year":"2021","journal-title":"Indian J. Nucl. Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1038\/s41585-019-0212-4","article-title":"Multiparametric MRI for Prostate Cancer Diagnosis: Current Status and Future Directions","volume":"17","author":"Stabile","year":"2019","journal-title":"Nat. Rev. Urol."},{"key":"ref_20","first-page":"303","article-title":"Magnetic Resonance Imaging for Localization of Prostate Cancer in the Setting of Biochemical Recurrence","volume":"34","author":"Panebianco","year":"2016","journal-title":"Urol. Oncol. Semin. Orig. Investig."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.semradonc.2022.10.002","article-title":"Seeing Is Believing: State of the Art Imaging of Bladder Cancer","volume":"33","author":"Messina","year":"2023","journal-title":"Semin. Radiat. Oncol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bardis, M.D., Houshyar, R., Chang, P.D., Ushinsky, A., Glavis-Bloom, J., Chahine, C., Bui, T.-L., Rupasinghe, M., Filippi, C.G., and Chow, D.S. (2020). Applications of Artificial Intelligence to Prostate Multiparametric MRI (MpMRI): Current and Emerging Trends. Cancers, 12.","DOI":"10.3390\/cancers12051204"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"095004","DOI":"10.1117\/1.JBO.27.9.095004","article-title":"Image-Guided Raman Spectroscopy Navigation System to Improve Transperineal Prostate Cancer Detection. Part 2: In-Vivo Tumor-Targeting Using a Classification Model Combining Spectral and MRI-Radiomics Features","volume":"27","author":"Grajales","year":"2022","journal-title":"J. Biomed. Opt."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1142\/S2339547818300020","article-title":"The Growing Role of Precision and Personalized Medicine for Cancer Treatment","volume":"6","author":"Krzyszczyk","year":"2018","journal-title":"Technology"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.canlet.2021.10.012","article-title":"Experimental Challenges to Modeling Prostate Cancer Heterogeneity","volume":"524","author":"Baena","year":"2022","journal-title":"Cancer Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1038\/nrclinonc.2016.76","article-title":"Translational and Clinical Implications of the Genetic Landscape of Prostate Cancer","volume":"13","author":"Spratt","year":"2016","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1007\/s00066-020-01663-3","article-title":"Radiomics in Radiation Oncology\u2014Basics, Methods, and Limitations","volume":"196","author":"Lohmann","year":"2020","journal-title":"Strahlenther. Onkol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"403","DOI":"10.3390\/curroncol31010027","article-title":"An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies","volume":"31","author":"Fusco","year":"2024","journal-title":"Curr. Oncol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Brocki, L., and Chung, N.C. (2023). Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models. Cancers, 15.","DOI":"10.3390\/cancers15092459"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1038\/s41571-021-00560-7","article-title":"Predicting Cancer Outcomes with Radiomics and Artificial Intel-ligence in Radiology","volume":"19","author":"Bera","year":"2021","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","article-title":"Machine Learning: Algorithms, Real-World Applications and Research Directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"100343","DOI":"10.1016\/j.medntd.2024.100343","article-title":"A Systematic Review on Recent Methods on Deep Learning for Automatic Detection of Alz-heimer\u2019s Disease","volume":"25","author":"Chamakuri","year":"2024","journal-title":"Med. Nov. Technol. Devices"},{"key":"ref_33","unstructured":"Misra, S., and Hao, L. (2019). Machine Learning for Subsurface Characterization, Gulf Professional Publishing. Chapter 7."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Taye, M.M. (2023). Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Di-rections. Computers, 12.","DOI":"10.3390\/computers12050091"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"100379","DOI":"10.1016\/j.cosrev.2021.100379","article-title":"A Survey on Deep Learning and Its Applications","volume":"40","author":"Dong","year":"2021","journal-title":"Comput. Sci. Rev."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1038\/s43856-022-00199-0","article-title":"Artificial Intelligence and Machine Learning in Cancer Imaging","volume":"2","author":"Koh","year":"2022","journal-title":"Commun. Med."},{"key":"ref_37","first-page":"272","article-title":"Artificial Intelligence and Machine Learning in Bioinformatics","volume":"1","author":"Lai","year":"2019","journal-title":"Encycl. Bioin-form. Comput. Biol."},{"key":"ref_38","first-page":"100623","article-title":"Autonomous Vehicular Overtaking Maneuver: A Survey and Taxonomy","volume":"42","author":"Lodhi","year":"2023","journal-title":"Veh. Commun."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/B0080-4270(73)00264-9","article-title":"Descriptive and Inferential Statistics","volume":"3","author":"Leon","year":"1998","journal-title":"Compr. Clin. Psychol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"100363","DOI":"10.1016\/j.imu.2020.100363","article-title":"A Review of Recent Approaches for Emotion Classification Using Elec-trocardiography and Electrodermography Signals","volume":"20","author":"Bulagang","year":"2020","journal-title":"Inform. Med. Unlocked"},{"key":"ref_41","unstructured":"Mohr, D.L., Wilson, W.J., and Freund, R.J. (2021). Statistical Methods, Academic Press. [4th ed.]."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/bs.host.2020.01.002","article-title":"Machine Learning Algorithms, Applications, and Practices in Data Science","volume":"43","author":"Yeturu","year":"2020","journal-title":"Handb. Stat."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Mesut, B., and Aksu, N.B. (2023). Role of artificial intelligence in quality profiling and optimization of drug products. A Handbook of Artificial Intelligence in Drug Delivery, Elsevier. Chapter 3.","DOI":"10.1016\/B978-0-323-89925-3.00003-4"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1080\/09553002.2018.1492756","article-title":"Rectal Wall MRI Radi-omics in Prostate Cancer Patients: Prediction of and Correlation with Early Rectal Toxicity","volume":"94","author":"Abdollahi","year":"2018","journal-title":"Int. J. Radiat. Biol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1007\/s11547-018-0966-4","article-title":"Machine Learning-Based Radiomic Models to Predict Intensity-Modulated Radiation Therapy Response, Gleason Score and Stage in Prostate Cancer","volume":"124","author":"Abdollahi","year":"2019","journal-title":"La Radiol. Medica"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.jmir.2018.12.002","article-title":"MRI Radiomic Analysis of IMRT-Induced Bladder Wall Changes in Prostate Cancer Patients: A Relationship with Radiation Dose and Toxicity","volume":"50","author":"Abdollahi","year":"2019","journal-title":"J. Med. Imaging Radiat. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"109593","DOI":"10.1016\/j.radonc.2023.109593","article-title":"Machine-Learning with Region-Level Radiomic and Dosimetric Features for Predicting Radiotherapy-Induced Rectal Toxicities in Prostate Cancer Patients","volume":"183","author":"Yang","year":"2023","journal-title":"Radiother. Oncol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"7199","DOI":"10.1007\/s00330-023-09642-9","article-title":"Role of Radiomic Analysis of [18F]Fluoromethylcholine PET\/CT in Predicting Biochemical Recurrence in a Cohort of Intermediate and High Risk Prostate Cancer Patients at Initial Staging","volume":"33","author":"Marturano","year":"2023","journal-title":"Eur. Radiol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1007\/s00261-023-04050-5","article-title":"Machine Learning Based on Radiomics Features Combing B-Mode Transrectal Ultrasound and Contrast-Enhanced Ultrasound to Improve Peripheral Zone Prostate Cancer Detection","volume":"49","author":"Sun","year":"2023","journal-title":"Abdom. Radiol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1002\/pros.24272","article-title":"Incorporating Artificial Intelligence in Urology: Supervised Machine Learning Algorithms Demonstrate Comparative Advantage over Nomograms in Predicting Biochemical Recurrence after Prostatectomy","volume":"82","author":"Tan","year":"2022","journal-title":"Prostate"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Luxton, J.J., McKenna, M.J., Lewis, A.M., Taylor, L.E., Jhavar, S.G., Swanson, G.P., and Bailey, S.M. (2021). Telomere Length Dynamics and Chromosomal Instability for Predicting Individual Radiosensitivity and Risk via Machine Learning. J. Pers. Med., 11.","DOI":"10.3390\/jpm11030188"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s11547-019-01082-0","article-title":"CT Imaging Markers to Improve Radiation Toxicity Prediction in Prostate Cancer Radiotherapy by Stacking Regres-sion Algorithm","volume":"125","author":"Mostafaei","year":"2020","journal-title":"La Radiol. Medica"},{"key":"ref_53","first-page":"2","article-title":"Artificial Intelligence Predictive Model for Hormone Therapy Use in Prostate Cancer","volume":"2","author":"Spratt","year":"2023","journal-title":"N. Engl. J. Med."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Massi, M.C., Gasperoni, F., Ieva, F., Paganoni, A.M., Zunino, P., Manzoni, A., Franco, N.R., Veldeman, L., Ost, P., and Fonteyne, V. (2020). A Deep Learning Approach Validates Genetic Risk Factors for Late Toxicity after Prostate Cancer Radiotherapy in a RE-QUITE Multi-National Cohort. Front. Oncol., 10.","DOI":"10.3389\/fonc.2020.541281"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.1007\/s13246-023-01260-5","article-title":"Radiomics Based Predictive Modeling of Rectal Toxicity in Prostate Cancer Patients Undergoing Radiotherapy: CT and MRI Comparison","volume":"46","author":"Hassaninejad","year":"2023","journal-title":"Phys. Eng. Sci. Med."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1016\/j.ijrobp.2018.04.044","article-title":"Radio-Pathomic Maps of Epithelium and Lumen Density Predict the Location of High-Grade Pros-tate Cancer","volume":"101","author":"McGarry","year":"2018","journal-title":"Int. J. Radiat. Oncol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"095016","DOI":"10.1088\/1361-6560\/acc77c","article-title":"Deep Learning Enables MV-Based Real-Time Image Guided Radiation Therapy for Prostate Cancer Patients","volume":"68","author":"Chrystall","year":"2023","journal-title":"Phys. Med. Biol."},{"key":"ref_58","first-page":"1","article-title":"Changes in Apparent Diffusion Coefficient Radiomics Features during Dose-Painted Radiotherapy and High Dose Rate Brachytherapy for Prostate Cancer","volume":"9","author":"Lee","year":"2018","journal-title":"Phys. Imaging Radiat. Oncol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1186\/s13014-021-01906-2","article-title":"The Feasibility of a Dose Painting Proce-dure to Treat Prostate Cancer Based on MpMR Images and Hierarchical Clustering","volume":"16","author":"Rezaeijo","year":"2021","journal-title":"Radiat. Oncol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1760","DOI":"10.1007\/s00330-020-07227-4","article-title":"Advanced Zoomed Diffusion-Weighted Imaging vs. Full-Field-of-View Diffusion-Weighted Imaging in Prostate Cancer Detection: A Radiomic Features Study","volume":"31","author":"Hu","year":"2020","journal-title":"Eur. Radiol."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Algohary, A., Alhusseini, M., Breto, A.L., Kwon, D., Xu, I.R., Gaston, S.M., Castillo, P., Punnen, S., Spieler, B., and Abramowitz, M.C. (2022). Longitudinal Changes and Predictive Value of Multiparametric MRI Features for Prostate Cancer Patients Treated with MRI-Guided Lattice Extreme Ablative Dose (LEAD) Boost Radiotherapy. Cancers, 14.","DOI":"10.3390\/cancers14184475"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.ijrobp.2018.01.054","article-title":"Machine Learning on a Genome-Wide Association Study to Predict Late Genitourinary Toxicity after Prostate Radiation Therapy","volume":"101","author":"Lee","year":"2018","journal-title":"Int. J. Radiat. Oncol."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s41512-022-00126-w","article-title":"Risk of Bias of Prognostic Models Developed Using Machine Learning: A Systematic Review in Oncology","volume":"6","author":"Dhiman","year":"2022","journal-title":"Diagn. Progn. Res."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"n2281","DOI":"10.1136\/bmj.n2281","article-title":"Risk of Bias in Studies on Prediction Models Developed Using Supervised Machine Learning Techniques: Systematic Review","volume":"375","author":"Navarro","year":"2021","journal-title":"BMJ"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.jclinepi.2021.06.017","article-title":"Large-Scale Validation of the Prediction Model Risk of Bias Assessment Tool (PROBAST) Using a Short Form: High Risk of Bias Models Show Poorer Discrimination","volume":"138","author":"Venema","year":"2021","journal-title":"J. Clin. Epidemiol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.ccm.2015.02.014","article-title":"Functional Imaging","volume":"36","author":"Mirsadraee","year":"2015","journal-title":"Clin. Chest Med."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2891","DOI":"10.1007\/s00261-024-04468-5","article-title":"Deep Learning-Based Image Quality Assessment: Impact on Detection Accuracy of Prostate Cancer Extraprostatic Extension on MRI","volume":"49","author":"Lin","year":"2024","journal-title":"Abdom. Radiol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"320","DOI":"10.2214\/AJR.17.17843","article-title":"Contrast-Enhanced Ultrasound of the Liver: Optimizing Technique and Clinical Applications","volume":"210","author":"Pang","year":"2018","journal-title":"Am. J. Roentgenol."},{"key":"ref_69","unstructured":"Mookherjee, S., and Mohan, S. (2021). Biomedical Imaging Instrumentation: Applications in Tissue, Cellular and Molecular Diagnostics, Elsevier."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1186\/s13244-022-01199-3","article-title":"Comparative Performance of Fully-Automated and Semi-Automated Artificial Intelligence Methods for the Detection of Clinically Significant Prostate Cancer on MRI: A Systematic Review","volume":"13","author":"Sushentsev","year":"2022","journal-title":"Insights Imaging"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"708","DOI":"10.3390\/analytics2030039","article-title":"Application of Machine Learning and Deep Learning Models in Prostate Cancer Diagnosis Using Medical Images: A Systematic Review","volume":"2","author":"Olabanjo","year":"2023","journal-title":"Analytics"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1186\/s13244-024-01783-9","article-title":"Optimizing Radiomics for Prostate Cancer Diagnosis: Feature Selection Strategies, Machine Learning Clas-sifiers, and MRI Sequences","volume":"15","author":"Mylona","year":"2024","journal-title":"Insights Imaging"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.1007\/s11547-025-02037-4","article-title":"Combining MRI Radiomics, Hypoxia Gene Signature Score and Clinical Variables for Prediction of Biochemical Recurrence-Free Survival after Radiotherapy in Prostate Cancer","volume":"130","author":"Zhong","year":"2025","journal-title":"Radiol. Med."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"175628722211090","DOI":"10.1177\/17562872221109020","article-title":"Radiomics in Prostate Cancer: An Up-To-Date Review","volume":"14","author":"Ferro","year":"2022","journal-title":"Ther. Adv. Urol."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Simpson, B.S., Morka, N., Freeman, A., Kirkham, A., Kelly, D., Whitaker, H.C., Emberton, M., and Norris, J.M. (2022). Comparison of Multiparametric Magnetic Resonance Imaging with Prostate-Specific Membrane Antigen Positron-Emission Tomography Imaging in Primary Prostate Cancer Diagnosis: A Systematic Review and Meta-Analysis. Cancers, 14.","DOI":"10.3390\/cancers14143497"},{"key":"ref_76","first-page":"436","article-title":"Ultrasound and Advanced Imaging Techniques in Prostate Cancer Diagnosis: A Comparative Study of MpMRI, TRUS, and PET\/CT","volume":"33","author":"Dong","year":"2025","journal-title":"J. X-Ray Sci. Technol."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"4355","DOI":"10.1016\/j.acra.2025.02.044","article-title":"Diagnostic Performance of Artificial Intelligence Based on Biparametric MRI for Clinically Significant Prostate Cancer: A Systematic Review and Meta-Analysis","volume":"32","author":"Yan","year":"2025","journal-title":"Acad. Radiol."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Ching, J.C.F., Lam, S., Lam, C.C.H., Lui, A.O.Y., Kwong, J.C.K., Lo, A.Y.H., Chan, J.W.H., Cai, J., Leung, W.S., and Lee, S.W.Y. (2023). Integrating CT-Based Radiomic Model with Clinical Features Improves Long-Term Prognostication in High-Risk Prostate Can-cer. Front. Oncol., 13.","DOI":"10.3389\/fonc.2023.1060687"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"20136","DOI":"10.1038\/s41598-022-24435-8","article-title":"Cone-Beam CT Delta-Radiomics to Predict Genitourinary Toxicities and International Prostate Symptom of Prostate Cancer Patients: A Pilot Study","volume":"12","author":"Delgadillo","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1007\/s00066-020-01677-x","article-title":"Cone-Beam Computed Tomography-Based Radiomics in Prostate Cancer: A Mono-Institutional Study","volume":"196","author":"Bosetti","year":"2020","journal-title":"Strahlenther. Onkol."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"2968","DOI":"10.1007\/s00259-020-04864-1","article-title":"A CT-Based Radi-omics Model to Detect Prostate Cancer Lymph Node Metastases in PSMA Radioguided Surgery Patients","volume":"47","author":"Peeken","year":"2020","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Gulum, M.A., Trombley, C.M., and Kantardzic, M. (2021). A Review of Explainable Deep Learning Cancer Detection Models in Medical Imaging. Appl. Sci., 11.","DOI":"10.3390\/app11104573"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: An updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"}],"container-title":["Tomography"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2379-139X\/12\/5\/62\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T11:41:52Z","timestamp":1777462912000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2379-139X\/12\/5\/62"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,28]]},"references-count":83,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["tomography12050062"],"URL":"https:\/\/doi.org\/10.3390\/tomography12050062","relation":{},"ISSN":["2379-139X"],"issn-type":[{"value":"2379-139X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,28]]}}}