{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T16:19:07Z","timestamp":1780589947534,"version":"3.54.1"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T00:00:00Z","timestamp":1748563200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T00:00:00Z","timestamp":1748563200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004203","name":"Ontario Institute for Cancer Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004203","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Abdom Radiol"],"DOI":"10.1007\/s00261-025-05019-2","type":"journal-article","created":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T23:45:05Z","timestamp":1748562305000},"page":"5924-5933","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Using AI to triage patients without clinically significant prostate cancer using biparametric MRI and PSA"],"prefix":"10.1007","volume":"50","author":[{"given":"Emerson P.","family":"Grabke","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carolina A. M.","family":"Heming","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amit","family":"Hadari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"Finelli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sangeet","family":"Ghai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katherine","family":"Lajkosz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Babak","family":"Taati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masoom A.","family":"Haider","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,30]]},"reference":[{"key":"5019_CR1","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.eururo.2019.08.005","volume":"77","author":"MBB Culp","year":"2020","unstructured":"Culp MBB, Soerjomataram I, Efstathiou JA, et al (2020) Recent Global Patterns in Prostate Cancer Incidence and Mortality Rates. Eur Urol 77:38\u201352. https:\/\/doi.org\/10.1016\/j.eururo.2019.08.005","journal-title":"Eur Urol"},{"key":"5019_CR2","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1097\/JU.0000000000003491","volume":"210","author":"JT Wei","year":"2023","unstructured":"Wei JT, Barocas D, Carlsson S, et al (2023) Early Detection of Prostate Cancer: AUA\/SUO Guideline Part I: Prostate Cancer Screening. J Urol 210:46\u201353. https:\/\/doi.org\/10.1097\/JU.0000000000003491","journal-title":"J Urol"},{"key":"5019_CR3","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1097\/JU.0000000000003492","volume":"210","author":"JT Wei","year":"2023","unstructured":"Wei JT, Barocas D, Carlsson S, et al (2023) Early Detection of Prostate Cancer: AUA\/SUO Guideline Part II: Considerations for a Prostate Biopsy. J Urol 210:54\u201363. https:\/\/doi.org\/10.1097\/JU.0000000000003492","journal-title":"J Urol"},{"key":"5019_CR4","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1016\/j.euo.2023.09.013","volume":"7","author":"L Klotz","year":"2024","unstructured":"Klotz L, Chin J, Black PC, et al (2024) Magnetic Resonance Imaging\u2013Targeted Versus Systematic Prostate Biopsies: 2-year Follow-up of a Prospective Randomized Trial (PRECISE). Eur Urol Oncol 7:456\u2013461. https:\/\/doi.org\/10.1016\/j.euo.2023.09.013","journal-title":"Eur Urol Oncol"},{"key":"5019_CR5","doi-asserted-by":"publisher","first-page":"1767","DOI":"10.1056\/NEJMoa1801993","volume":"378","author":"V Kasivisvanathan","year":"2018","unstructured":"Kasivisvanathan V, Rannikko AS, Borghi M, et al (2018) MRI-Targeted or Standard Biopsy for Prostate-Cancer Diagnosis. N Engl J Med 378:1767\u20131777. https:\/\/doi.org\/10.1056\/NEJMoa1801993","journal-title":"N Engl J Med"},{"key":"5019_CR6","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/S1470-2045(18)30569-2","volume":"20","author":"O Rouvi\u00e8re","year":"2019","unstructured":"Rouvi\u00e8re O, Puech P, Renard-Penna R, et al (2019) Use of prostate systematic and targeted biopsy on the basis of multiparametric MRI in biopsy-naive patients (MRI-FIRST): a prospective, multicentre, paired diagnostic study. Lancet Oncol 20:100\u2013109. https:\/\/doi.org\/10.1016\/S1470-2045(18)30569-2","journal-title":"Lancet Oncol"},{"key":"5019_CR7","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1016\/j.eururo.2018.11.023","volume":"75","author":"M Van Der Leest","year":"2019","unstructured":"Van Der Leest M, Cornel E, Isra\u00ebl B, et al (2019) Head-to-head Comparison of Transrectal Ultrasound-guided Prostate Biopsy Versus Multiparametric Prostate Resonance Imaging with Subsequent Magnetic Resonance-guided Biopsy in Biopsy-na\u00efve Men with Elevated Prostate-specific Antigen: A Large Prospective Multicenter Clinical Study. Eur Urol 75:570\u2013578. https:\/\/doi.org\/10.1016\/j.eururo.2018.11.023","journal-title":"Eur Urol"},{"key":"5019_CR8","doi-asserted-by":"publisher","DOI":"10.1097\/JU.0000000000000518","author":"D Deniffel","year":"2020","unstructured":"Deniffel D, Zhang Y, Salinas E, et al (2020) Reducing Unnecessary Prostate Multiparametric Magnetic Resonance Imaging by Using Clinical Parameters to Predict Negative and Indeterminate Findings. J Urol. https:\/\/doi.org\/10.1097\/JU.0000000000000518","journal-title":"J Urol"},{"key":"5019_CR9","doi-asserted-by":"publisher","first-page":"1362","DOI":"10.1016\/j.urology.2014.02.035","volume":"83","author":"DP Ankerst","year":"2014","unstructured":"Ankerst DP, Hoefler J, Bock S, et al (2014) Prostate cancer prevention trial risk calculator 2.0 for the prediction of low- vs high-grade prostate cancer. Urology 83:1362\u20131368. https:\/\/doi.org\/10.1016\/j.urology.2014.02.035","journal-title":"Urology"},{"key":"5019_CR10","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.eururo.2018.05.003","volume":"74","author":"DP Ankerst","year":"2018","unstructured":"Ankerst DP, Straubinger J, Selig K, et al (2018) A Contemporary Prostate Biopsy Risk Calculator Based on Multiple Heterogeneous Cohorts. Eur Urol 74:197\u2013203. https:\/\/doi.org\/10.1016\/j.eururo.2018.05.003","journal-title":"Eur Urol"},{"key":"5019_CR11","doi-asserted-by":"publisher","unstructured":"Carbunaru S, Nettey OS, Gogana P, et al (2019) A comparative effectiveness analysis of the PBCG vs. PCPT risks calculators in a multi-ethnic cohort. BMC Urol 19:121. https:\/\/doi.org\/10.1186\/s12894-019-0553-6","DOI":"10.1186\/s12894-019-0553-6"},{"key":"5019_CR12","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1111\/bju.15835","volume":"131","author":"HD Patel","year":"2023","unstructured":"Patel HD, Koehne EL, Shea SM, et al (2023) A prostate biopsy risk calculator based on MRI: development and comparison of the Prospective Loyola University multiparametric MRI (PLUM) and Prostate Biopsy Collaborative Group (PBCG) risk calculators. Bju Int 131:227\u2013235. https:\/\/doi.org\/10.1111\/bju.15835","journal-title":"Bju Int"},{"key":"5019_CR13","doi-asserted-by":"publisher","DOI":"10.1148\/rycan.240078","volume":"7","author":"C Roest","year":"2025","unstructured":"Roest C, Kwee TC, De Jong IJ, et al (2025) Development and Validation of a Deep Learning Model Based on MRI and Clinical Characteristics to Predict Risk of Prostate Cancer Progression. Radiol Imaging Cancer 7:e240078. https:\/\/doi.org\/10.1148\/rycan.240078","journal-title":"Radiol Imaging Cancer"},{"key":"5019_CR14","doi-asserted-by":"publisher","first-page":"7909","DOI":"10.1007\/s00330-024-10818-0","volume":"34","author":"A Schrader","year":"2024","unstructured":"Schrader A, Netzer N, Hielscher T, et al (2024) Prostate cancer risk assessment and avoidance of prostate biopsies using fully automatic deep learning in prostate MRI: comparison to PI-RADS and integration with clinical data in nomograms. Eur Radiol 34:7909\u20137920. https:\/\/doi.org\/10.1007\/s00330-024-10818-0","journal-title":"Eur Radiol"},{"key":"5019_CR15","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1016\/S1470-2045(24)00220-1","volume":"25","author":"A Saha","year":"2024","unstructured":"Saha A, Bosma JS, Twilt JJ, et al (2024) Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study. Lancet Oncol 25:879\u2013887. https:\/\/doi.org\/10.1016\/S1470-2045(24)00220-1","journal-title":"Lancet Oncol"},{"key":"5019_CR16","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1016\/j.neuroimage.2006.01.015","volume":"31","author":"PA Yushkevich","year":"2006","unstructured":"Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-Guided 3D Active Contour Segmentation of Anatomical Structures: Significantly Improved Efficiency and Reliability. Neuroimage 31:1116\u20131128. https:\/\/doi.org\/10.1016\/j.neuroimage.2006.01.015","journal-title":"Neuroimage"},{"key":"5019_CR17","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee F, Jaeger PF, Kohl SAA, et al (2021) nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18:203\u2013211. https:\/\/doi.org\/10.1038\/s41592-020-01008-z","journal-title":"Nat Methods"},{"key":"5019_CR18","doi-asserted-by":"publisher","unstructured":"Yusim I, Krenawi M, Mazor E, et al (2020) The use of prostate specific antigen density to predict clinically significant prostate cancer. Sci Rep 1\u20136. https:\/\/doi.org\/10.1038\/s41598-020-76786-9","DOI":"10.1038\/s41598-020-76786-9"},{"key":"5019_CR19","unstructured":"Cardoso MJ, Li W, Brown R, et al (2022) MONAI: An open-source framework for deep learning in healthcare"},{"key":"5019_CR20","first-page":"371","volume-title":"Statistical Atlases and Computational Models of the Heart","author":"E Kerfoot","year":"2019","unstructured":"Kerfoot E, Clough J, Oksuz I, et al (2019) Left-Ventricle Quantification Using Residual U-Net. In: Pop M, Sermesant M, Zhao J, et al (eds) Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. Springer International Publishing, Cham, pp 371\u2013380"},{"key":"5019_CR21","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Deep Residual Learning for Image Recognition. Comput Vis Pattern Recognit","DOI":"10.1109\/CVPR.2016.90"},{"key":"5019_CR22","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","volume":"42","author":"TY Lin","year":"2020","unstructured":"Lin TY, Goyal P, Girshick R, et al (2020) Focal Loss for Dense Object Detection. IEEE Trans Pattern Anal Mach Intell 42:318\u2013327. https:\/\/doi.org\/10.1109\/TPAMI.2018.2858826","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5019_CR23","unstructured":"Loshchilov I, Hutter F (2019) Decoupled weight decay regularization. 7th Int Conf Learn Represent ICLR 2019"},{"key":"5019_CR24","unstructured":"Loshchilov I, Hutter F (2017) SGDR: Stochastic gradient descent with warm restarts. 5th Int Conf Learn Represent ICLR 2017 - Conf Track Proc 1\u201316"},{"key":"5019_CR25","doi-asserted-by":"publisher","unstructured":"Bosma JS, Saha A, Hosseinzadeh M, et al (2023) Semisupervised Learning with Report-guided Pseudo Labels for Deep Learning\u2013based Prostate Cancer Detection Using Biparametric MRI. Radiol Artif Intell 5:. https:\/\/doi.org\/10.1148\/ryai.230031","DOI":"10.1148\/ryai.230031"},{"key":"5019_CR26","doi-asserted-by":"publisher","unstructured":"Oerther B, Nedelcu A, Engel H, et al (2024) Update on PI-RADS Version 2.1 Diagnostic Performance Benchmarks for Prostate MRI: Systematic Review and Meta-Analysis. Radiology 312:e233337. https:\/\/doi.org\/10.1148\/radiol.233337","DOI":"10.1148\/radiol.233337"},{"key":"5019_CR27","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1111\/j.0006-341X.2000.00345.x","volume":"56","author":"W Leisenring","year":"2000","unstructured":"Leisenring W, Alono T, Pepe MS (2000) Comparisons of Predictive Values of Binary Medical Diagnostic Tests for Paired Designs. Biometrics 56:345\u2013351. https:\/\/doi.org\/10.1111\/j.0006-341X.2000.00345.x","journal-title":"Biometrics"},{"key":"5019_CR28","doi-asserted-by":"publisher","first-page":"5866","DOI":"10.1109\/TPAMI.2021.3074313","volume":"44","author":"D Zhang","year":"2022","unstructured":"Zhang D, Han J, Cheng G, Yang MH (2022) Weakly Supervised Object Localization and Detection: A Survey. IEEE Trans Pattern Anal Mach Intell 44:5866\u20135885. https:\/\/doi.org\/10.1109\/TPAMI.2021.3074313","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5019_CR29","doi-asserted-by":"publisher","unstructured":"Savage CH, Tanwar M, Elkassem AA, et al (2024) Prospective Evaluation of Artificial Intelligence Triage of Intracranial Hemorrhage on Noncontrast Head CT Examinations. Am J Roentgenol AJR.24.31639. https:\/\/doi.org\/10.2214\/AJR.24.31639","DOI":"10.2214\/AJR.24.31639"},{"key":"5019_CR30","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1038\/s41586-019-1799-6","volume":"577","author":"SM McKinney","year":"2020","unstructured":"McKinney SM, Sieniek M, Godbole V, et al (2020) International evaluation of an AI system for breast cancer screening. Nature 577:89\u201394. https:\/\/doi.org\/10.1038\/s41586-019-1799-6","journal-title":"Nature"}],"container-title":["Abdominal Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00261-025-05019-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00261-025-05019-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00261-025-05019-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T17:04:48Z","timestamp":1762880688000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00261-025-05019-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,30]]},"references-count":30,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["5019"],"URL":"https:\/\/doi.org\/10.1007\/s00261-025-05019-2","relation":{},"ISSN":["2366-0058"],"issn-type":[{"value":"2366-0058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,30]]},"assertion":[{"value":"4 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 April 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 May 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 May 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}