{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T03:28:19Z","timestamp":1791430099888,"version":"4.3.3"},"reference-count":72,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T00:00:00Z","timestamp":1762473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Economic Development of the Russian Federation","award":["139-15-2025-004"],"award-info":[{"award-number":["139-15-2025-004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Background: Approximately half of prostate cancer (PCa) patients present with low- or intermediate-risk disease eligible for active surveillance (AS). However, a substantial proportion of individuals experience pathological progression during follow-up. In this study, we developed predictive models for histopathological PCa progression in patients on AS. Methods: The dataset comprised patients with biopsy-confirmed PCa and a minimum follow-up of two years. All patients underwent regular surveillance, including prostate-specific antigen (PSA) measurements and MRI examinations. Each patient had three to six consecutive MRI scans available for analysis. Histopathological progression was defined as an upgrade to a higher grade group on repeat targeted biopsy. Predictive modeling integrated radiomic and clinical variables using machine learning (ML). SHapley Additive exPlanations (SHAP) was used for feature interpretation. Results: Three models were obtained: (1) a baseline model utilizing radiomic features from initial MRI scans combined with baseline PSA density (PSAd) (AUC = 0.793, sensitivity = 0.690, specificity = 0.830); (2) a delta model incorporating feature changes between latest and baseline available MRI scans with final PSAd (AUC = 0.913, sensitivity = 0.793, specificity = 0.936); and (3) a time series model analyzing the complete series of radiomic features and PSAd (AUC = 0.917, sensitivity = 0.828, specificity = 0.894). Conclusions: Our predictive models demonstrated strong performance in distinguishing progressors from non-progressors, suggesting that radiomic analysis combined with ML has significant potential to enhance PCa management. This approach could enable more personalized treatment strategies and improve clinical decision-making for patients undergoing AS.<\/jats:p>","DOI":"10.3390\/cancers17223598","type":"journal-article","created":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T10:56:45Z","timestamp":1762513005000},"page":"3598","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Improving the Potential for Predicting Prostate Cancer Progression in Patients on Active Surveillance Using Explainable Artificial Intelligence"],"prefix":"10.3390","volume":"17","author":[{"given":"Olga","family":"Vershinina","sequence":"first","affiliation":[{"name":"Research Center in Artificial Intelligence, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia"},{"name":"Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikita","family":"Sushentsev","sequence":"additional","affiliation":[{"name":"Department of Radiology, Addenbrooke\u2019s Hospital and University of Cambridge, Cambridge CB2 0QQ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7540-1130","authenticated-orcid":false,"given":"Alexey","family":"Zaikin","sequence":"additional","affiliation":[{"name":"Institute for Cognitive Neuroscience, University Higher School of Economics, Moscow 101000, Russia"},{"name":"Department of Mathematics and Women\u2019s Cancer, University College London, London WC1E 6BT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0194-6389","authenticated-orcid":false,"given":"Oleg","family":"Blyuss","sequence":"additional","affiliation":[{"name":"Centre for Cancer Screening, Prevention and Early Diagnosis, Wolfson Institute of Population Health, Queen Mary University of London, London EC1M 6BQ, UK"},{"name":"Department of Paediatrics and Paediatric Infectious Diseases, Institute of Child\u2019s Health, Sechenov First Moscow State Medical University (Sechenov University), Moscow 119991, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tristan","family":"Barrett","sequence":"additional","affiliation":[{"name":"Department of Radiology, Addenbrooke\u2019s Hospital and University of Cambridge, Cambridge CB2 0QQ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mikhail","family":"Ivanchenko","sequence":"additional","affiliation":[{"name":"Research Center in Artificial Intelligence, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia"},{"name":"Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,7]]},"reference":[{"key":"ref_1","first-page":"229","article-title":"Global Cancer Statistics 2022: GLOBOCAN 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","doi-asserted-by":"crossref","first-page":"S14","DOI":"10.1016\/j.juro.2007.03.135","article-title":"Contemporary Trends in Low Risk Prostate Cancer: Risk Assessment and Treatment","volume":"178","author":"Cooperberg","year":"2007","journal-title":"J. Urol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2809","DOI":"10.21037\/tau-20-1370","article-title":"Active Surveillance for Prostate Cancer","volume":"10","author":"Shill","year":"2021","journal-title":"Transl. Androl. Urol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2084","DOI":"10.1001\/jama.2024.6695","article-title":"Long-Term Outcomes in Patients Using Protocol-Directed Active Surveillance for Prostate Cancer","volume":"331","author":"Newcomb","year":"2024","journal-title":"JAMA"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1056\/NEJMoa1615869","article-title":"Follow-up of Prostatectomy versus Observation for Early Prostate Cancer","volume":"377","author":"Wilt","year":"2017","journal-title":"N. Engl. J. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2373","DOI":"10.1001\/jama.2010.1720","article-title":"Active Surveillance Compared With Initial Treatment for Men With Low-Risk Prostate Cancer: A Decision Analysis","volume":"304","author":"Hayes","year":"2010","journal-title":"JAMA"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1016\/j.eururo.2014.10.028","article-title":"How Does Active Surveillance for Prostate Cancer Affect Quality of Life? A Systematic Review","volume":"67","author":"Bellardita","year":"2015","journal-title":"Eur. Urol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.juro.2007.05.038","article-title":"Predictors of Histological Disease Progression in Untreated, Localized Prostate Cancer","volume":"178","author":"Venkitaraman","year":"2007","journal-title":"J. Urol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2664","DOI":"10.1002\/cncr.23502","article-title":"Active Surveillance for the Management of Prostate Cancer in a Contemporary Cohort","volume":"112","author":"Konety","year":"2008","journal-title":"Cancer"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1200\/JCO.2009.24.2180","article-title":"Clinical Results of Long-Term Follow-Up of a Large, Active Surveillance Cohort With Localized Prostate Cancer","volume":"28","author":"Klotz","year":"2010","journal-title":"J. Clin. Oncol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.juro.2010.09.115","article-title":"Risk Stratification and Validation of Prostate Specific Antigen Density as Independent Predictor of Progression in Men With Low Risk Prostate Cancer During Active Surveillance","volume":"185","author":"Werner","year":"2011","journal-title":"J. Urol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1016\/j.eururo.2012.11.005","article-title":"Active Surveillance for Low-Risk Prostate Cancer Worldwide: The PRIAS Study","volume":"63","author":"Bul","year":"2013","journal-title":"Eur. Urol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1016\/j.eururo.2016.06.007","article-title":"A Decade of Active Surveillance in the PRIAS Study: An Update and Evaluation of the Criteria Used to Recommend a Switch to Active Treatment","volume":"70","author":"Bokhorst","year":"2016","journal-title":"Eur. Urol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.eururo.2017.03.009","article-title":"Low-Risk Prostate Cancer: Identification, Management, and Outcomes","volume":"72","author":"Moschini","year":"2017","journal-title":"Eur. Urol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1016\/j.eururo.2018.10.025","article-title":"Reasons for Discontinuing Active Surveillance: Assessment of 21 Centres in 12 Countries in the Movember GAP3 Consortium","volume":"75","author":"Ji","year":"2019","journal-title":"Eur. Urol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s00345-021-03622-8","article-title":"Active Surveillance for Prostate Cancer: Selection Criteria, Guidelines, and Outcomes","volume":"40","author":"Walker","year":"2022","journal-title":"World J. Urol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1016\/j.eururo.2013.05.049","article-title":"Systematic Review of Complications of Prostate Biopsy","volume":"64","author":"Loeb","year":"2013","journal-title":"Eur. Urol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"ofw265","DOI":"10.1093\/ofid\/ofw265","article-title":"Infection-Related Hospital Admissions After Prostate Biopsy in United States Men","volume":"4","author":"Evans","year":"2017","journal-title":"Open Forum Infect. Dis."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1016\/j.eururo.2016.06.011","article-title":"Reporting Magnetic Resonance Imaging in Men on Active Surveillance for Prostate Cancer: The PRECISE Recommendations\u2014A Report of a European School of Oncology Task Force","volume":"71","author":"Moore","year":"2017","journal-title":"Eur. Urol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"131","DOI":"10.2214\/AJR.16.16355","article-title":"The Emerging Role of MRI in Prostate Cancer Active Surveillance and Ongoing Challenges","volume":"208","author":"Barrett","year":"2017","journal-title":"Am. J. Roentgenol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"990","DOI":"10.1016\/j.juro.2018.01.002","article-title":"Clinically Localized Prostate Cancer: AUA\/ASTRO\/SUO Guideline. Part II: Recommended Approaches and Details of Specific Care Options","volume":"199","author":"Sanda","year":"2018","journal-title":"J. Urol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1111\/bju.14166","article-title":"Progression and Treatment Rates Using an Active Surveillance Protocol Incorporating Image-guided Baseline Biopsies and Multiparametric Magnetic Resonance Imaging Monitoring for Men with Favourable-risk Prostate Cancer","volume":"122","author":"Thurtle","year":"2018","journal-title":"BJU Int."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1028","DOI":"10.1097\/JU.0000000000002885","article-title":"Final Analysis of the Magnetic Resonance Imaging in Active Surveillance Trial","volume":"208","author":"Doan","year":"2022","journal-title":"J. Urol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1016\/j.eururo.2021.05.001","article-title":"Reliability of Serial Prostate Magnetic Resonance Imaging to Detect Prostate Cancer Progression During Active Surveillance: A Systematic Review and Meta-Analysis","volume":"80","author":"Rajwa","year":"2021","journal-title":"Eur. Urol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sushentsev, N., Rundo, L., Blyuss, O., Gnanapragasam, V.J., Sala, E., and Barrett, T. (2021). MRI-Derived Radiomics Model for Baseline Prediction of Prostate Cancer Progression on Active Surveillance. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-92341-6"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"78","DOI":"10.26502\/jrci.2809061","article-title":"Radiomic Features on Prostatic Multiparametric Magnetic Resonance Imaging Enable Progression Risk in Patients on Active Surveillance: A Pilot Study","volume":"5","author":"Totaro","year":"2022","journal-title":"J. Radiol. Clin. Imaging"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1007\/s00330-021-08151-x","article-title":"Comparative Performance of MRI-Derived PRECISE Scores and Delta-Radiomics Models for the Prediction of Prostate Cancer Progression in Patients on Active Surveillance","volume":"32","author":"Sushentsev","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Midya, A., Hiremath, A., Huber, J., Sankar Viswanathan, V., Omil-Lima, D., Mahran, A., Bittencourt, L.K., Harsha Tirumani, S., Ponsky, L., and Shiradkar, R. (2023). Delta Radiomic Patterns on Serial Bi-Parametric MRI Are Associated with Pathologic Upgrading in Prostate Cancer Patients on Active Surveillance: Preliminary Findings. Front. Oncol., 13.","DOI":"10.3389\/fonc.2023.1166047"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3792","DOI":"10.1007\/s00330-023-09438-x","article-title":"Time Series Radiomics for the Prediction of Prostate Cancer Progression in Patients on Active Surveillance","volume":"33","author":"Sushentsev","year":"2023","journal-title":"Eur. Radiol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1111\/bju.12132","article-title":"Definitions of Terms, Processes and a Minimum Dataset for Transperineal Prostate Biopsies: A Standardization Approach of the Ginsburg Study Group for Enhanced Prostate Diagnostics","volume":"112","author":"Kuru","year":"2013","journal-title":"BJU Int."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"894.e1","DOI":"10.1016\/j.crad.2019.06.004","article-title":"Three-Year Experience of a Dedicated Prostate mpMRI Pre-Biopsy Programme and Effect on Timed Cancer Diagnostic Pathways","volume":"74","author":"Barrett","year":"2019","journal-title":"Clin. Radiol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1116","DOI":"10.1016\/j.neuroimage.2006.01.015","article-title":"User-Guided 3D Active Contour Segmentation of Anatomical Structures: Significantly Improved Efficiency and Reliability","volume":"31","author":"Yushkevich","year":"2006","journal-title":"NeuroImage"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","article-title":"Computational Radiomics System to Decode the Radiographic Phenotype","volume":"77","author":"Fedorov","year":"2017","journal-title":"Cancer Res."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zwanenburg, A., Leger, S., Agolli, L., Pilz, K., Troost, E.G.C., Richter, C., and L\u00f6ck, S. (2019). Assessing Robustness of Radiomic Features by Image Perturbation. Sci. Rep., 9.","DOI":"10.1038\/s41598-018-36938-4"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"46","DOI":"10.5489\/cuaj.329","article-title":"PSA Density Is Superior than PSA and Gleason Score for Adverse Pathologic Features Prediction in Patients with Clinically Localized Prostate Cancer","volume":"6","author":"Sfoungaristos","year":"2012","journal-title":"Can. Urol. Assoc. J."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.juro.2013.09.034","article-title":"Population Based Study of Predictors of Adverse Pathology among Candidates for Active Surveillance with Gleason 6 Prostate Cancer","volume":"191","author":"Vellekoop","year":"2014","journal-title":"J. Urol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1111\/bju.13836","article-title":"Risk-stratification Based on Magnetic Resonance Imaging and Prostate-specific Antigen Density May Reduce Unnecessary Follow-up Biopsy Procedures in Men on Active Surveillance for Low-risk Prostate Cancer","volume":"120","author":"Alberts","year":"2017","journal-title":"BJU Int."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2696","DOI":"10.1007\/s00330-020-07336-0","article-title":"MRI-Derived PRECISE Scores for Predicting Pathologically-Confirmed Radiological Progression in Prostate Cancer Patients on Active Surveillance","volume":"31","author":"Caglic","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Sammut, C., and Webb, G.I. (2011). Encyclopedia of Machine Learning, Springer Science & Business Media.","DOI":"10.1007\/978-0-387-30164-8"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1883","DOI":"10.4249\/scholarpedia.1883","article-title":"K-Nearest Neighbor","volume":"4","author":"Peterson","year":"2009","journal-title":"Scholarpedia"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"12","DOI":"10.11613\/BM.2014.003","article-title":"Understanding Logistic Regression Analysis","volume":"24","author":"Sperandei","year":"2014","journal-title":"Biochem. Med."},{"key":"ref_42","unstructured":"Vapnik, V.N. (1998). Statistical Learning Theory, Wiley."},{"key":"ref_43","unstructured":"Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. (1984). Classification and Regression Trees, Chapman & Hall\/CRC."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic Gradient Boosting","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_47","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017, January 4\u20139). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_48","unstructured":"Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., and Gulin, A. (2017). CatBoost: Unbiased Boosting with Categorical Features. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_50","unstructured":"Bergstra, J.S., Bardenet, R., Bengio, Y., and K\u00e9gl, B. (2011, January 12\u201314). Algorithms for Hyper-Parameter Optimization. Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS 2011), Granada, Spain."},{"key":"ref_51","unstructured":"Watanabe, S. (2023). Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M. (2019). Optuna: A Next-Generation Hyperparameter Optimization Framework. arXiv.","DOI":"10.1145\/3292500.3330701"},{"key":"ref_53","unstructured":"Lundberg, S.M., and Lee, S.I. (2017, January 4\u20139). A Unified Approach to Interpreting Model Predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Chandramouli, S., Leo, P., Lee, G., Elliott, R., Davis, C., Zhu, G., Fu, P., Epstein, J.I., Veltri, R., and Madabhushi, A. (2020). Computer Extracted Features from Initial H&E Tissue Biopsies Predict Disease Progression for Prostate Cancer Patients on Active Surveillance. Cancers, 12.","DOI":"10.3390\/cancers12092708"},{"key":"ref_55","first-page":"161.e1","article-title":"A Machine Learning Approach to Predict Progression on Active Surveillance for Prostate Cancer","volume":"40","author":"Nayan","year":"2022","journal-title":"Urol. Oncol. Semin. Orig. Investig."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhou, H., Xie, M., Shi, H., Shou, C., Tang, M., Zhang, Y., Hu, Y., and Liu, X. (2025). Integrating Multimodal Imaging and Peritumoral Features for Enhanced Prostate Cancer Diagnosis: A Machine Learning Approach. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0323752"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zhao, W., Hou, M., Wang, J., Song, D., and Niu, Y. (2024). Interpretable Machine Learning Model for Predicting Clinically Significant Prostate Cancer: Integrating Intratumoral and Peritumoral Radiomics with Clinical and Metabolic Features. BMC Med. Imaging, 24.","DOI":"10.1186\/s12880-024-01548-2"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Liu, X., Liu, R., He, H., Yan, Y., Zhang, L., and Zhang, Q. (2025). Multi-Regional Multiparametric Deep Learning Radiomics for Diagnosis of Clinically Significant Prostate Cancer. J. Digit. Imaging. Inform. Med.","DOI":"10.1007\/s10278-025-01551-1"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Nicoletti, G., Mazzetti, S., Maimone, G., Cignini, V., Cuocolo, R., Faletti, R., Gatti, M., Imbriaco, M., Longo, N., and Ponsiglione, A. (2024). Development and Validation of an Explainable Radiomics Model to Predict High-Aggressive Prostate Cancer: A Multicenter Radiomics Study Based on Biparametric MRI. Cancers, 16.","DOI":"10.3390\/cancers16010203"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Vraka, A., Marfil-Trujillo, M., Ribas-Despuig, G., Flor-Arnal, S., Cerd\u00e1-Alberich, L., Jim\u00e9nez-G\u00f3mez, P., Jimenez-Pastor, A., and Mart\u00ed-Bonmat\u00ed, L. (2025). Radiomics-Based Early Triage of Prostate Cancer: A Multicenter Study from the CHAIMELEON Project 2025. medRxiv.","DOI":"10.1101\/2025.04.29.25326685"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Li, Z., Yang, L., Wang, X., Xu, H., Chen, W., Kang, S., Huang, Y., Shu, C., Cui, F., and Zhang, Y. (2025). Intralesional and Perilesional Radiomics Strategy Based on Different Machine Learning for the Prediction of International Society of Urological Pathology Grade Group in Prostate Cancer. BMC Med. Imaging, 25.","DOI":"10.1186\/s12880-025-01812-z"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"3730","DOI":"10.1007\/s00259-025-07248-5","article-title":"Interpretable Multimodal Deep Learning Model for Predicting Post-Surgical International Society of Urological Pathology Grade in Primary Prostate Cancer","volume":"52","author":"Jiang","year":"2025","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Liu, B., Ding, X., Xu, H., Bai, X., Zhang, X., Cui, M., Guo, A., Mu, X., Xie, L., and Kang, H. (2025). Preoperative Assessment of Extraprostatic Extension in Prostate Cancer Using an Interpretable Tabular Prior-Data Fitted Network-Based Radiomics Model From MRI. Magn. Reson. Imaging.","DOI":"10.1002\/jmri.70111"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1038\/pcan.2010.36","article-title":"Relationship between Initial PSA Density with Future PSA Kinetics and Repeat Biopsies in Men with Prostate Cancer on Active Surveillance","volume":"14","author":"Kotb","year":"2011","journal-title":"Prostate Cancer Prostatic Dis."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"20210842","DOI":"10.1259\/bjr.20210842","article-title":"Serial Changes in Tumour Measurements and Apparent Diffusion Coefficients in Prostate Cancer Patients on Active Surveillance with and without Histopathological Progression","volume":"95","author":"Sushentsev","year":"2022","journal-title":"Br. J. Radiol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1007\/s00259-021-05430-z","article-title":"The Added Value of PSMA PET\/MR Radiomics for Prostate Cancer Staging","volume":"49","author":"Solari","year":"2022","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Faiella, E., Pileri, M., Ragone, R., De Nicola, A.M., Beomonte Zobel, B., Grasso, R.F., and Santucci, D. (2025). Promising Results About the Possibility to Identify Prostate Cancer Patients Employing a Random Forest Classifier: A Preliminary Study Preoperative Patients Selection. Diagnostics, 15.","DOI":"10.3390\/diagnostics15040421"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"2840","DOI":"10.1007\/s00330-015-3701-8","article-title":"Haralick Texture Analysis of Prostate MRI: Utility for Differentiating Non-Cancerous Prostate from Prostate Cancer and Differentiating Prostate Cancers with Different Gleason Scores","volume":"25","author":"Wibmer","year":"2015","journal-title":"Eur. Radiol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1111\/bju.16743","article-title":"A 10-year Analysis of MRI-driven Prostate Cancer Diagnosis and Active Surveillance: Trends and Implications","volume":"136","author":"Sushentsev","year":"2025","journal-title":"BJU Int."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Cattell, R., Chen, S., and Huang, C. (2019). Robustness of radiomic features in magnetic resonance imaging: Review and a phantom study. Vis. Comput. Ind. Biomed. Art, 2.","DOI":"10.1186\/s42492-019-0025-6"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.1002\/mp.14038","article-title":"T2w-MRI signal normalization affects radiomics features reproducibility","volume":"47","author":"Scalco","year":"2020","journal-title":"Med. Phys."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"P\u0159ibil, J., P\u0159ibilov\u00e1, A., and Frollo, I. (2019). Analysis of the influence of different settings of scan sequence parameters on vibration and noise generated in the open-air MRI scanning area. Sensors, 19.","DOI":"10.3390\/s19194198"}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/17\/22\/3598\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T11:15:44Z","timestamp":1762514144000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/17\/22\/3598"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,7]]},"references-count":72,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["cancers17223598"],"URL":"https:\/\/doi.org\/10.3390\/cancers17223598","relation":{},"ISSN":["2072-6694"],"issn-type":[{"value":"2072-6694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,7]]}}}