{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:23:18Z","timestamp":1777695798922,"version":"3.51.4"},"reference-count":32,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T00:00:00Z","timestamp":1772755200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Current Oncology"],"abstract":"<jats:p>Background: Multiparametric MRI is central to detecting clinically significant prostate cancer (csPCa), but diagnostic accuracy depends on reader experience. Artificial intelligence (AI) tools may support prostate MRI interpretation and reduce inter-reader variability. This study compared the detection rate of a trial, non-commercial version an AI-based software (PAROS) with that of an experienced radiologist. Methods: This retrospective single-center study included 150 patients who underwent prostate MRI followed by combined systematic and MRI-targeted transperineal biopsy. MRI examinations were interpreted by an experienced radiologist according to PI-RADS v2.1 and independently analyzed using a precommercial trial version of PAROS operating on biparametric MRI. Histopathology served as the reference standard. Detection rate was evaluated using sensitivity, specificity, and positive and negative likelihood ratios (PLR and NLR) at PI-RADS thresholds \u22653 and \u22654. Results: CsPCa was present in 63.3% of patients. At both PI-RADS thresholds, PAROS and the radiologist showed comparable sensitivity and specificity, wuth extremely low NLRs, indicating excellent rule-out capability. PLRs were modest and similar at PI-RADS \u2265 3 (1.26 vs. 1.42) and 1.88 for both at PI-RADS \u2265 4. PAROS detected more lesions, particularly in the transition zone. Conclusions: PAROS achieved csPCa detection comparable to an experienced radiologist, supporting its role as a decision-support tool in prostate MRI interpretation.<\/jats:p>","DOI":"10.3390\/curroncol33030151","type":"journal-article","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T09:22:14Z","timestamp":1772788934000},"page":"151","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence in Prostate MRI: Comparison of an AI-Based Software and an Experienced Radiologist for Detecting Clinically Significant Prostate Cancer"],"prefix":"10.3390","volume":"33","author":[{"given":"Roberto","family":"Castellana","sequence":"first","affiliation":[{"name":"Department of Radiology, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9845-4400","authenticated-orcid":false,"given":"Simona","family":"Marzi","sequence":"additional","affiliation":[{"name":"Medical Physics Laboratory, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrea","family":"Russo","sequence":"additional","affiliation":[{"name":"Department of Pathology, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria","family":"Ferriero","sequence":"additional","affiliation":[{"name":"Department of Urology, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0187-9323","authenticated-orcid":false,"given":"Irene","family":"Terrenato","sequence":"additional","affiliation":[{"name":"Biostatistics-Scientific Direction, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eugenia","family":"Papaleo","sequence":"additional","affiliation":[{"name":"Clinical Engineering and Technology and Informatic Systems, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giuseppe","family":"Navanteri","sequence":"additional","affiliation":[{"name":"Clinical Engineering and Technology and Informatic Systems, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Davide","family":"Vitale","sequence":"additional","affiliation":[{"name":"Department of Radiology, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giuseppe","family":"Pizzi","sequence":"additional","affiliation":[{"name":"Department of Radiology, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6203-4642","authenticated-orcid":false,"given":"Antonello","family":"Vidiri","sequence":"additional","affiliation":[{"name":"Department of Radiology, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luca","family":"Bertini","sequence":"additional","affiliation":[{"name":"Department of Radiology, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,6]]},"reference":[{"key":"ref_1","first-page":"12","article-title":"Cancer statistics, 2024","volume":"74","author":"Siegel","year":"2024","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","unstructured":"OECD\/European Commission (2026, January 01). Health at a Glance: Europe 2024 STATE OF HEALTH IN THE EU CYCLE. Available online: https:\/\/www.oecd-ilibrary.org\/docserver\/b3704e14-en.pdf?expires=1733593180&id=id&accname=guest&checksum=42F45899616ADDF9B08DE13CFD4AFF7A."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1547","DOI":"10.1056\/NEJMoa2214122","article-title":"Fifteen-Year Outcomes after Monitoring, Surgery, or Radiotherapy for Prostate Cancer","volume":"388","author":"Hamdy","year":"2023","journal-title":"N. Engl. J. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1016\/S1470-2045(24)00220-1","article-title":"Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): An international, paired, non-inferiority, confirmatory study","volume":"25","author":"Saha","year":"2024","journal-title":"Lancet Oncol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1097\/JU.0000000000003492","article-title":"Early Detection of Prostate Cancer: AUA\/SUO Guideline Part II: Considerations for a Prostate Biopsy","volume":"210","author":"Wei","year":"2023","journal-title":"J. Urol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.eururo.2024.03.027","article-title":"EAU-EANM-ESTRO-ESUR-ISUP-SIOG Guidelines on Prostate Cancer\u20142024 Update. Part I: Screening, Diagnosis, and Local Treatment with Curative Intent","volume":"86","author":"Cornford","year":"2024","journal-title":"Eur. Urol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1767","DOI":"10.1056\/NEJMoa1801993","article-title":"MRI-Targeted or Standard Biopsy for Prostate-Cancer Diagnosis","volume":"378","author":"Kasivisvanathan","year":"2018","journal-title":"N. Engl. J. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.eururo.2019.02.033","article-title":"Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2","volume":"76","author":"Turkbey","year":"2019","journal-title":"Eur. Urol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e233337","DOI":"10.1148\/radiol.233337","article-title":"Update on PI-RADS Version 2.1 Diagnostic Performance Benchmarks for Prostate MRI: Systematic Review and Meta-Analysis","volume":"312","author":"Oerther","year":"2024","journal-title":"Radiology"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1694","DOI":"10.1002\/jmri.26555","article-title":"Intra- and interreader reproducibility of PI-RADSv2: A multireader study","volume":"49","author":"Smith","year":"2019","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"H\u00f6tker, A.M., Bl\u00fcthgen, C., Rupp, N.J., Schneider, A.F., Eberli, D., and Donati, O.F. (2020). Comparison of the PI-RADS 2.1 scoring system to PI-RADS 2.0: Impact on diagnostic accuracy and inter-reader agreement. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0239975"},{"key":"ref_12","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":"Am. J. Roentgenol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"109894","DOI":"10.1016\/j.ejrad.2021.109894","article-title":"Detection and PI-RADS classification of focal lesions in prostate MRI: Performance comparison between a deep learning-based algorithm (DLA) and radiologists with various levels of experience","volume":"142","author":"Youn","year":"2021","journal-title":"Eur. J. Radiol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"100625","DOI":"10.1016\/j.ejro.2024.100625","article-title":"An online national quality assessment survey of prostate MRI reading: Interreader variability in prostate volume measurement and PI-RADS classification","volume":"14","author":"Thimansson","year":"2025","journal-title":"Eur. J. Radiol. Open"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1186\/s40644-020-00351-w","article-title":"Expected impact of MRI-related interreader variability on ProScreen prostate cancer screening trial: A pre-trial validation study","volume":"20","author":"Hietikko","year":"2020","journal-title":"Cancer Imaging"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5404","DOI":"10.1007\/s00330-020-06929-z","article-title":"ESUR\/ESUI consensus statements on multi-parametric MRI for the detection of clinically significant prostate cancer: Quality requirements for image acquisition, interpretation and radiologists\u2019 training","volume":"30","author":"Tummers","year":"2020","journal-title":"Eur. Radiol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.euros.2022.11.021","article-title":"Is Artificial Intelligence Replacing Our Radiology Stars in Prostate Magnetic Resonance Imaging? The Stars Do Not Look Big, But They Can Look Brighter","volume":"48","author":"Giganti","year":"2023","journal-title":"Eur. Urol. Open Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2010","DOI":"10.3390\/tomography8040168","article-title":"Quantib Prostate Compared to an Expert Radiologist for the Diagnosis of Prostate Cancer on mpMRI: A Single-Center Preliminary Study","volume":"8","author":"Faiella","year":"2022","journal-title":"Tomography"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"111790","DOI":"10.1016\/j.ejrad.2024.111790","article-title":"Assessment of a fully-automated diagnostic AI software in prostate MRI: Clinical evaluation and histopathological correlation","volume":"181","author":"Bayerl","year":"2024","journal-title":"Eur. J. Radiol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4915","DOI":"10.1007\/s00330-024-11323-0","article-title":"AI-powered prostate cancer detection: A multi-centre, multi-scanner validation study","volume":"35","author":"Giganti","year":"2025","journal-title":"Eur. Radiol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"110749","DOI":"10.1016\/j.ejrad.2023.110749","article-title":"Bridging the experience gap in prostate multiparametric magnetic resonance imaging using artificial intelligence: A prospective multi-reader comparison study on inter-reader agreement in PI-RADS v2.1, image quality and reporting time between novice and expert readers","volume":"161","author":"Forookhi","year":"2023","journal-title":"Eur. J. Radiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e232788","DOI":"10.1148\/radiol.232788","article-title":"MRI-based Deep Learning Algorithm for Assisting Clinically Significant Prostate Cancer Detection: A Bicenter Prospective Study","volume":"314","author":"Lee","year":"2025","journal-title":"Radiology"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.mri.2017.10.011","article-title":"Conventional vs. reduced field of view diffusion weighted imaging of the prostate: Comparison of image quality, correlation with histology, and inter-reader agreement","volume":"47","author":"Warndahl","year":"2018","journal-title":"Magn. Reson. Imaging"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4082","DOI":"10.1007\/s00330-017-4800-5","article-title":"Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer","volume":"27","author":"Wang","year":"2017","journal-title":"Eur. Radiol."},{"key":"ref_25","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_26","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_27","doi-asserted-by":"crossref","unstructured":"European Society of R (2022). Current practical experience with artificial intelligence in clinical radiology: A survey of the European Society of Radiology. Insights Imaging, 13, 107.","DOI":"10.1186\/s13244-022-01247-y"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"7728","DOI":"10.1007\/s00330-024-10909-y","article-title":"Prostate-MRI reporting should be done with the aid of AI systems: Pros","volume":"34","author":"Penzkofer","year":"2024","journal-title":"Eur. Radiol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1007\/s00330-024-11012-y","article-title":"Patient perspectives on the use of artificial intelligence in prostate cancer diagnosis on MRI","volume":"35","author":"Fransen","year":"2025","journal-title":"Eur. Radiol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Jin, S., Wu, L., Liang, Z., and Yan, W. (2023). The prognostic value of zonal origin in clinically localized prostate cancer: A systematic review and meta-analysis. Front. Oncol., 13.","DOI":"10.3389\/fonc.2023.1248222"},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"2215","DOI":"10.1002\/jmri.29031","article-title":"Deep Learning-Based T2-Weighted MR Image Quality Assessment and Its Impact on Prostate Cancer Detection Rates","volume":"59","author":"Lin","year":"2023","journal-title":"J. Magn. Reson. Imaging"}],"container-title":["Current Oncology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1718-7729\/33\/3\/151\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:18:28Z","timestamp":1777454308000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1718-7729\/33\/3\/151"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,6]]},"references-count":32,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["curroncol33030151"],"URL":"https:\/\/doi.org\/10.3390\/curroncol33030151","relation":{},"ISSN":["1718-7729"],"issn-type":[{"value":"1718-7729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,6]]}}}