{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T23:14:41Z","timestamp":1783379681054,"version":"3.54.6"},"reference-count":17,"publisher":"Elsevier BV","issue":"8","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Urologic Oncology: Seminars and Original Investigations"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.urolonc.2026.05.013","type":"journal-article","created":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T09:14:22Z","timestamp":1780650862000},"page":"127-131","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence in prostate cancer imaging: A mini-review of current applications and future directions"],"prefix":"10.1016","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8960-8164","authenticated-orcid":false,"given":"Mehmet Ali","family":"Erg\u00fcl","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3166-9968","authenticated-orcid":false,"given":"Volkan","family":"Sabur","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"13","key":"10.1016\/j.urolonc.2026.05.013_bib0001","doi-asserted-by":"crossref","DOI":"10.3390\/cancers13133318","article-title":"Artificial intelligence compared to radiologists for the initial diagnosis of prostate cancer on Magnetic resonance imaging: a systematic review and recommendations for future studies","volume":"13","author":"Syer","year":"2021","journal-title":"Cancers (Basel)"},{"issue":"10","key":"10.1016\/j.urolonc.2026.05.013_bib0002","article-title":"Comparative performance of deep learning and radiologists for the diagnosis and localization of clinically significant prostate cancer at MRI: a systematic review","volume":"12","author":"Roest","year":"2022","journal-title":"Life (Basel)"},{"issue":"4","key":"10.1016\/j.urolonc.2026.05.013_bib0003","article-title":"Current value of biparametric prostate MRI with machine-learning or deep-learning in the detection, grading, and characterization of prostate cancer: a systematic review","volume":"12","author":"Michaely","year":"2022","journal-title":"Diagnostics (Basel)"},{"issue":"1","key":"10.1016\/j.urolonc.2026.05.013_bib0004","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1186\/s13244-022-01340-2","article-title":"Automatic segmentation of prostate zonal anatomy on MRI: a systematic review of the literature","volume":"13","author":"Wu","year":"2022","journal-title":"Insights Imaging"},{"issue":"17","key":"10.1016\/j.urolonc.2026.05.013_bib0005","doi-asserted-by":"crossref","DOI":"10.3390\/cancers16172951","article-title":"The role of radiomics in the prediction of clinically significant prostate cancer in the PI-RADS v2 and v2.1 era: a systematic review","volume":"16","author":"Antolin","year":"2024","journal-title":"Cancers (Basel)"},{"issue":"6","key":"10.1016\/j.urolonc.2026.05.013_bib0006","doi-asserted-by":"crossref","DOI":"10.3390\/cancers12061606","article-title":"Automated classification of significant prostate cancer on MRI: a systematic review on the performance of machine learning applications","volume":"12","author":"Castillo T","year":"2020","journal-title":"Cancers (Basel)"},{"issue":"22","key":"10.1016\/j.urolonc.2026.05.013_bib0007","article-title":"Systematic review of AI-assisted MRI in prostate cancer diagnosis: enhancing accuracy through second opinion tools","volume":"14","author":"Alqahtani","year":"2024","journal-title":"Diagnostics (Basel)"},{"issue":"6","key":"10.1016\/j.urolonc.2026.05.013_bib0008","doi-asserted-by":"crossref","first-page":"3981","DOI":"10.1007\/s00330-023-10427-3","article-title":"Radiomics for the identification of extraprostatic extension with prostate MRI: a systematic review and meta-analysis","volume":"34","author":"Ponsiglione","year":"2024","journal-title":"Eur Radiol"},{"key":"10.1016\/j.urolonc.2026.05.013_bib0009","article-title":"Application of radiomics-based prediction model to predict preoperative lymph node metastasis in prostate cancer: a systematic review and meta-analysis","volume":"15","author":"Zheng","year":"2025","journal-title":"Front Oncol"},{"issue":"1","key":"10.1016\/j.urolonc.2026.05.013_bib0010","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/s41747-022-00282-0","article-title":"Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics","volume":"6","author":"Liberini","year":"2022","journal-title":"Eur Radiol Exp"},{"issue":"22","key":"10.1016\/j.urolonc.2026.05.013_bib0011","doi-asserted-by":"crossref","DOI":"10.3390\/jcm12227032","article-title":"Can machine learning models detect and predict lymph node involvement in prostate cancer? A comprehensive systematic review","volume":"12","author":"Faiella","year":"2023","journal-title":"J Clin Med"},{"issue":"2","key":"10.1016\/j.urolonc.2026.05.013_bib0012","doi-asserted-by":"crossref","first-page":"69","DOI":"10.62347\/CSIJ8326","article-title":"Integration of magnetic resonance imaging and deep learning for prostate cancer detection: a systematic review","volume":"13","author":"Kumar","year":"2025","journal-title":"Am J Clin Exp Urol"},{"issue":"4","key":"10.1016\/j.urolonc.2026.05.013_bib0013","doi-asserted-by":"crossref","DOI":"10.2196\/22394","article-title":"Radiomic and genomic machine learning method performance for prostate cancer diagnosis: systematic literature review","volume":"23","author":"Castaldo","year":"2021","journal-title":"J Med Internet Res"},{"issue":"7","key":"10.1016\/j.urolonc.2026.05.013_bib0014","article-title":"Quality of multicenter studies using MRI radiomics for diagnosing clinically significant prostate cancer: a systematic review","volume":"12","author":"Bleker","year":"2022","journal-title":"Life (Basel)"},{"issue":"1","key":"10.1016\/j.urolonc.2026.05.013_bib0015","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1038\/s41746-025-01591-5","article-title":"Advancing breast, lung and prostate cancer research with federated learning. A systematic review","volume":"8","author":"Ankolekar","year":"2025","journal-title":"NPJ Digit Med"},{"issue":"7","key":"10.1016\/j.urolonc.2026.05.013_bib0016","doi-asserted-by":"crossref","DOI":"10.3390\/jpm15070284","article-title":"Prostate MRI using deep learning reconstruction in response to cancer screening demands-A systematic review and meta-analysis","volume":"15","author":"Ursprung","year":"2025","journal-title":"J Pers Med"},{"issue":"1","key":"10.1016\/j.urolonc.2026.05.013_bib0017","doi-asserted-by":"crossref","first-page":"1581","DOI":"10.1186\/s12885-024-13320-4","article-title":"Survival prediction of glioblastoma patients using machine learning and deep learning: a systematic review","volume":"24","author":"Poursaeed","year":"2024","journal-title":"BMC Cancer"}],"container-title":["Urologic Oncology: Seminars and Original Investigations"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1078143926005302?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1078143926005302?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T22:28:10Z","timestamp":1783376890000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1078143926005302"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":17,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2026,8]]}},"alternative-id":["S1078143926005302"],"URL":"https:\/\/doi.org\/10.1016\/j.urolonc.2026.05.013","relation":{},"ISSN":["1078-1439"],"issn-type":[{"value":"1078-1439","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial intelligence in prostate cancer imaging: A mini-review of current applications and future directions","name":"articletitle","label":"Article Title"},{"value":"Urologic Oncology: Seminars and Original Investigations","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.urolonc.2026.05.013","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}