{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T13:29:55Z","timestamp":1783344595953,"version":"3.54.6"},"reference-count":64,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T00:00:00Z","timestamp":1753228800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"H2020 European Union\u2019s Horizon 2020 research and INCISIVE innovation program","award":["952179"],"award-info":[{"award-number":["952179"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Prostate cancer (PCa) is the most common malignancy in men. Precise grading is crucial for the effective treatment approaches of PCa. Machine learning (ML) applied to biparametric Magnetic Resonance Imaging (bpMRI) radiomics holds promise for improving PCa diagnosis and prognosis. This study investigated the efficiency of seven ML models to diagnose the different PCa grades, changing the input variables. Our studied sample comprised 214 men who underwent bpMRI in different imaging centers. Seven ML algorithms were compared using radiomic features extracted from T2-weighted (T2W) and diffusion-weighted (DWI) MRI, with and without the inclusion of Prostate-Specific Antigen (PSA) values. The performance of the models was evaluated using the receiver operating characteristic curve analysis. The models\u2019 performance was strongly dependent on the input parameters. Radiomic features derived from T2WI and DWI, whether used independently or in combination, demonstrated limited clinical utility, with AUC values ranging from 0.703 to 0.807. However, incorporating the PSA index significantly improved the models\u2019 efficiency, regardless of lesion location or degree of malignancy, resulting in AUC values ranging from 0.784 to 1.00. There is evidence that ML methods, in combination with radiomic analysis, can contribute to solving differential diagnostic problems of prostate cancers. Also, optimization of the analysis method is critical, according to the results of our study.<\/jats:p>","DOI":"10.3390\/jimaging11080250","type":"journal-article","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T10:49:17Z","timestamp":1753267757000},"page":"250","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["The Role of Radiomic Analysis and Different Machine Learning Models in Prostate Cancer Diagnosis"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1290-1802","authenticated-orcid":false,"given":"Eleni","family":"Bekou","sequence":"first","affiliation":[{"name":"Medical Physics Laboratory, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3665-5271","authenticated-orcid":false,"given":"Ioannis","family":"Seimenis","sequence":"additional","affiliation":[{"name":"Medical Physics Laboratory, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Athanasios","family":"Tsochatzis","sequence":"additional","affiliation":[{"name":"Ygeia Private Hospital, 15123 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karafyllia","family":"Tziagkana","sequence":"additional","affiliation":[{"name":"Department of Radiology, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikolaos","family":"Kelekis","sequence":"additional","affiliation":[{"name":"Research Unit of Radiology and Medical Imaging, 2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Savas","family":"Deftereos","sequence":"additional","affiliation":[{"name":"Department of Radiology, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikolaos","family":"Courcoutsakis","sequence":"additional","affiliation":[{"name":"Department of Radiology, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2324-699X","authenticated-orcid":false,"given":"Michael I.","family":"Koukourakis","sequence":"additional","affiliation":[{"name":"Department of Radiotherapy\/Oncology, University Hospital of Alexandroupolis, Democritus University of Thrace, 68100 Alexandroupolis, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Efstratios","family":"Karavasilis","sequence":"additional","affiliation":[{"name":"Medical Physics Laboratory, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,23]]},"reference":[{"key":"ref_1","unstructured":"(2024, April 10). Cancer Today. Available online: https:\/\/gco.iarc.who.int\/today\/."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1016\/j.eururo.2016.08.003","article-title":"EAU-ESTRO-SIOG Guidelines on Prostate Cancer. Part 1: Screening, Diagnosis, and Local Treatment with Curative Intent","volume":"71","author":"Mottet","year":"2017","journal-title":"Eur. Urol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.eururo.2020.09.046","article-title":"EAU-EANM-ESTRO-ESUR-SIOG Guidelines on Prostate Cancer. Part II-2020 Update: Treatment of Relapsing and Metastatic Prostate Cancer","volume":"79","author":"Cornford","year":"2021","journal-title":"Eur. Urol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1016\/j.juro.2018.05.016","article-title":"Prostate Specific Antigen Density as a Predictor of Clinically Significant Prostate Cancer When the Prostate Specific Antigen is in the Diagnostic Gray Zone: Defining the Optimum Cutoff Point Stratified by Race and Body Mass Index","volume":"200","author":"Aminsharifi","year":"2018","journal-title":"J. Urol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Merriel, S.W.D., Pocock, L., Gilbert, E., Creavin, S., Walter, F.M., Spencer, A., and Hamilton, W. (2022). Systematic review and meta-analysis of the diagnostic accuracy of prostate-specific antigen (PSA) for the detection of prostate cancer in symptomatic patients. BMC Med., 20.","DOI":"10.1186\/s12916-021-02230-y"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.eururo.2016.03.015","article-title":"Impact of Prostate-specific Antigen (PSA) Screening Trials and Revised PSA Screening Guidelines on Rates of Prostate Biopsy and Postbiopsy Complications","volume":"71","author":"Gershman","year":"2017","journal-title":"Eur. Urol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1890","DOI":"10.1002\/jmri.27008","article-title":"Multiparametric MRI-Based Radiomics for Prostate Cancer Screening With PSA in 4-10 ng\/mL to Reduce Unnecessary Biopsies","volume":"51","author":"Qi","year":"2020","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1002\/jmri.26243","article-title":"Prostate Cancer Differentiation and Aggressiveness: Assessment With a Radiomic-Based Model vs. PI-RADS v2","volume":"49","author":"Chen","year":"2019","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1002\/jmri.22790","article-title":"Predictive value of MRI in the localization, staging, volume estimation, assessment of aggressiveness, and guidance of radiotherapy and biopsies in prostate cancer","volume":"35","author":"Yakar","year":"2012","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_10","unstructured":"(2024, March 28). PI-RADS | American College of Radiology. Available online: https:\/\/www.acr.org\/Clinical-Resources\/Clinical-Tools-and-Reference\/Reporting-and-Data-Systems\/PI-RADS."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"141","DOI":"10.2214\/AJR.20.24199","article-title":"PI-RADS Versions 2 and 2.1: Interobserver Agreement and Diagnostic Performance in Peripheral and Transition Zone Lesions Among Six Radiologists","volume":"217","author":"Bhayana","year":"2021","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"17562872221109020","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_14","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: Images Are More than Pictures, They Are Data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"109438","DOI":"10.1016\/j.ejso.2024.109438","article-title":"The use of artificial intelligence in surgical oncology simulation","volume":"50","author":"Mulita","year":"2024","journal-title":"Eur. J. Surg. Oncol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Varghese, B., Chen, F., Hwang, D., Palmer, S.L., De Castro Abreu, A.L., Ukimura, O., Aron, M., Aron, M., Gill, I., and Duddalwar, V. (2019). Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images. Sci. Rep., 9.","DOI":"10.1038\/s41598-018-38381-x"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.1007\/s00330-019-06488-y","article-title":"Multiparametric MRI and auto-fixed volume of interest-based radiomics signature for clinically significant peripheral zone prostate cancer","volume":"30","author":"Bleker","year":"2020","journal-title":"Eur. Radiol."},{"key":"ref_18","unstructured":"(2023, December 31). ITK-SNAP Home. Available online: http:\/\/www.itksnap.org\/pmwiki\/pmwiki.php."},{"key":"ref_19","unstructured":"(2024, April 18). SimpleITK\u2014Home. Available online: https:\/\/simpleitk.org\/."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1007\/s00330-024-11093-9","article-title":"ESR Essentials: Radiomics\u2014Practice recommendations by the European Society of Medical Imaging Informatics","volume":"35","author":"Santinha","year":"2025","journal-title":"Eur. Radiol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1800","DOI":"10.1002\/jmri.28935","article-title":"The Effect of Image Resampling on the Performance of Radiomics-Based Artificial Intelligence in Multicenter Prostate MRI","volume":"59","author":"Bleker","year":"2024","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_22","unstructured":"(2023, October 17). Pyradiomics v3.1.0. Available online: https:\/\/github.com\/AIM-Harvard\/pyradiomics."},{"key":"ref_23","unstructured":"IBSI (2023, October 17). IBSI\u2014Image Biomarker Standardisation Initiative. Available online: https:\/\/theibsi.github.io\/."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","article-title":"Radiomics: The bridge between medical imaging and personalized medicine","volume":"14","author":"Lambin","year":"2017","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/s13244-020-00887-2","article-title":"Radiomics in medical imaging\u2014\u201chow-to\u201d guide and critical reflection","volume":"11","author":"Cester","year":"2020","journal-title":"Insights Imaging"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"35","DOI":"10.2152\/jmi.66.35","article-title":"Standardization of imaging features for radiomics analysis","volume":"66","author":"Haga","year":"2019","journal-title":"J. Med. Investig."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1186\/s40644-020-00311-4","article-title":"How to develop a meaningful radiomic signature for clinical use in oncologic patients","volume":"20","author":"Papanikolaou","year":"2020","journal-title":"Cancer Imaging"},{"key":"ref_28","unstructured":"Ljubljana, B.L. (2024, June 06). University of Orange Data Mining. Available online: https:\/\/orangedatamining.com."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.biosystems.2018.12.009","article-title":"Gene expression cancer classification using modified K-Nearest Neighbors technique","volume":"176","author":"Ayyad","year":"2019","journal-title":"Biosystems"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Muthukrishnan, R., and Rohini, R. (2016, January 24). LASSO: A feature selection technique in predictive modeling for machine learning. Proceedings of the 2016 IEEE International Conference on Advances in Computer Applications (ICACA), Coimbatore, India.","DOI":"10.1109\/ICACA.2016.7887916"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Nematollahi, H., Moslehi, M., Aminolroayaei, F., Maleki, M., and Shahbazi-Gahrouei, D. (2023). Diagnostic Performance Evaluation of Multiparametric Magnetic Resonance Imaging in the Detection of Prostate Cancer with Supervised Machine Learning Methods. Diagnostics, 13.","DOI":"10.3390\/diagnostics13040806"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ide, H., and Kurita, T. (2017, January 14\u201319). Improvement of learning for CNN with ReLU activation by sparse regularization. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966185"},{"key":"ref_33","unstructured":"(2025, July 04). Python Release Python 3.9.0. Available online: https:\/\/www.python.org\/downloads\/release\/python-390\/."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"6241","DOI":"10.1007\/s00330-024-10699-3","article-title":"Can we predict pathology without surgery? Weighing the added value of multiparametric MRI and whole prostate radiomics in integrative machine learning models","volume":"34","author":"Marvaso","year":"2024","journal-title":"Eur. Radiol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Dominguez, I., Rios-Ibacache, O., Caprile, P., Gonzalez, J., San Francisco, I.F., and Besa, C. (2023). MRI-Based Surrogate Imaging Markers of Aggressiveness in Prostate Cancer: Development of a Machine Learning Model Based on Radiomic Features. Diagnostics, 13.","DOI":"10.3390\/diagnostics13172779"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Gong, L., Xu, M., Fang, M., He, B., Li, H., Fang, X., Dong, D., and Tian, J. (2022). The potential of prostate gland radiomic features in identifying the Gleason score. Comput. Biol. Med., 144.","DOI":"10.1016\/j.compbiomed.2022.105318"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lu, Y., Li, B., Huang, H., Leng, Q., Wang, Q., Zhong, R., Huang, Y., Li, C., Yuan, R., and Zhang, Y. (2022). Biparametric MRI-based radiomics classifiers for the detection of prostate cancer in patients with PSA serum levels of 4\u223c10 ng\/mL. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.1020317"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Antolin, A., Roson, N., Mast, R., Arce, J., Almodovar, R., Cortada, R., Maceda, A., Escobar, M., Trilla, E., and Morote, J. (2024). The Role of Radiomics in the Prediction of Clinically Significant Prostate Cancer in the PI-RADS v2 and v2.1 Era: A Systematic Review. Cancers, 16.","DOI":"10.3390\/cancers16172951"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Jin, P., Shen, J., Yang, L., Zhang, J., Shen, A., Bao, J., and Wang, X. (2023). Machine learning-based radiomics model to predict benign and malignant PI-RADS v2.1 category 3 lesions: A retrospective multi-center study. BMC Medical Imaging, 23.","DOI":"10.1186\/s12880-023-01002-9"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Jing, G., Xing, P., Li, Z., Ma, X., Lu, H., Shao, C., Lu, Y., Lu, J., and Shen, F. (2022). Prediction of clinically significant prostate cancer with a multimodal MRI-based radiomics nomogram. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.918830"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e222276","DOI":"10.1148\/radiol.222276","article-title":"Interactive Explainable Deep Learning Model Informs Prostate Cancer Diagnosis at MRI","volume":"307","author":"Hamm","year":"2023","journal-title":"Radiology"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Castillo, T.J.M., Starmans, M.P.A., Arif, M., Niessen, W.J., Klein, S., Bangma, C.H., Schoots, I.G., and Veenland, J.F. (2021). A Multi-Center, Multi-Vendor Study to Evaluate the Generalizability of a Radiomics Model for Classifying Prostate cancer: High Grade vs. Low Grade. Diagnostics, 11.","DOI":"10.3390\/diagnostics11020369"},{"key":"ref_43","unstructured":"Raschka, S. (2020). Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.ejrad.2017.11.001","article-title":"Support Vector Machines (SVM) classification of prostate cancer Gleason score in central gland using multiparametric magnetic resonance images: A cross-validated study","volume":"98","author":"Li","year":"2018","journal-title":"Eur. J. Radiol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"6877","DOI":"10.1007\/s00330-020-07027-w","article-title":"Machine learning for the identification of clinically significant prostate cancer on MRI: A meta-analysis","volume":"30","author":"Cuocolo","year":"2020","journal-title":"Eur. Radiol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1515\/med-2021-0238","article-title":"Accurate diagnosis of prostate cancer using logistic regression","volume":"16","author":"Hooshmand","year":"2021","journal-title":"Open Med."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Ge, P., Gao, F., and Chen, G. (2015, January 2\u20135). Predictive models for prostate cancer based on logistic regression and artificial neural network. Proceedings of the 2015 IEEE International Conference on Mechatronics and Automation (ICMA), Beijing, China.","DOI":"10.1109\/ICMA.2015.7237702"},{"key":"ref_48","unstructured":"Namdar, K., Gujrathi, I., Haider, M.A., and Khalvati, F. (2019). Evolution-based Fine-tuning of CNNs for Prostate Cancer Detection 2019. arxiv."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"7575","DOI":"10.1007\/s00330-021-07856-3","article-title":"MRI index lesion radiomics and machine learning for detection of extraprostatic extension of disease: A multicenter study","volume":"31","author":"Cuocolo","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Yoo, S., Gujrathi, I., Haider, M.A., and Khalvati, F. (2019). Prostate Cancer Detection using Deep Convolutional Neural Networks. Sci. Rep., 9.","DOI":"10.1038\/s41598-019-55972-4"},{"key":"ref_51","unstructured":"Hashem, H., Alsakar, Y., Elgarayhi, A., Elmogy, M., and Sallah, M. (2022). An Enhanced Deep Learning Technique for Prostate Cancer Identification Based on MRI Scans. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4322","DOI":"10.1200\/JCO.2005.11.136","article-title":"Improved detection of prostate cancer using classification and regression tree analysis","volume":"23","author":"Garzotto","year":"2005","journal-title":"J. Clin. Oncol."},{"key":"ref_53","first-page":"43","article-title":"Decision Tree Analysis for Prostate Cancer Prediction in Patients with Serum PSA 10 ng\/ml or Less","volume":"21","author":"Pantic","year":"2020","journal-title":"Exp. Appl. Biomed. Res. (EABR)"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"20220238","DOI":"10.1259\/bjr.20220238","article-title":"Radiomic-based machine learning model for the accurate prediction of prostate cancer risk stratification","volume":"96","author":"Shu","year":"2023","journal-title":"Br. J. Radiol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1148\/rg.2018170147","article-title":"Multiparametric MR Imaging of the Prostate after Treatment of Prostate Cancer","volume":"38","author":"Patel","year":"2018","journal-title":"RadioGraphics"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"24355","DOI":"10.21037\/tau.2018.03.02","article-title":"Active surveillance review: Contemporary selection criteria, follow-up, compliance and outcomes","volume":"7","author":"Komisarenko","year":"2018","journal-title":"Transl. Androl. Urol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"756","DOI":"10.2214\/AJR.15.14912","article-title":"Assessment of Prostate Cancer Aggressiveness by Use of the Combination of Quantitative DWI and Dynamic Contrast-Enhanced MRI","volume":"206","author":"Mazaheri","year":"2016","journal-title":"Am. J. Roentgenol."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Twilt, J.J., van Leeuwen, K.G., Huisman, H.J., F\u00fctterer, J.J., and de Rooij, M. (2021). Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review. Diagnostics, 11.","DOI":"10.3390\/diagnostics11060959"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"100256","DOI":"10.1016\/j.imu.2019.100256","article-title":"Automated grading of prostate cancer using convolutional neural network and ordinal class classifier","volume":"17","author":"Abraham","year":"2019","journal-title":"Inform. Med. Unlocked"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"127","DOI":"10.18383\/j.tom.2018.00033","article-title":"Gleason Probability Maps: A Radiomics Tool for Mapping Prostate Cancer Likelihood in MRI Space","volume":"5","author":"McGarry","year":"2019","journal-title":"Tomography"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Chaddad, A., Kucharczyk, M.J., Cheddad, A., Clarke, S.E., Hassan, L., Ding, S., Rathore, S., Zhang, M., Katib, Y., and Bahoric, B. (2021). Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review. Cancers, 13.","DOI":"10.3390\/cancers13030552"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Nketiah, G.A., Elschot, M., Scheenen, T.W., Maas, M.C., Bathen, T.F., and Seln\u00e6s, K.M. (2021). Utility of T2-weighted MRI texture analysis in assessment of peripheral zone prostate cancer aggressiveness: A single-arm, multicenter study. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-81272-x"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1002\/acm2.12542","article-title":"Assessment of prostate cancer prognostic Gleason grade group using zonal-specific features extracted from biparametric MRI using a KNN classifier","volume":"20","author":"Jensen","year":"2019","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"E6265","DOI":"10.1073\/pnas.1505935112","article-title":"Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images","volume":"112","author":"Fehr","year":"2015","journal-title":"Proc. Natl. Acad. Sci. USA"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/8\/250\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:14:32Z","timestamp":1760033672000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/8\/250"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,23]]},"references-count":64,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["jimaging11080250"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11080250","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,23]]}}}