{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T18:28:46Z","timestamp":1782584926843,"version":"3.54.5"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T00:00:00Z","timestamp":1771545600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T00:00:00Z","timestamp":1771545600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To develop and retrospectively validate an artificial intelligence-based decision support system (AI-DSS) for optimising prostate biopsy decisions and improving benefit-to-harm ratios.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>This retrospective, multicentre, multiscanner study used data from 1022 patients. An AI-DSS integrating PI-RADS scores, automated prostate-specific antigen density (PSAd), and deep-learning imaging risk scores was developed on 770 cases and validated on an independent cohort of 252 men from six UK centres. The AI-DSS performance was benchmarked against the real-world clinical decisions (reference standard) using grade selectivity, biopsy efficiency, and selective biopsy avoidance as outcome measures. Biopsy-proven detection of grade group (GG)\u2009\u2265\u20092 disease was the reference standard.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In the validation cohort of 252 patients (mean age, 67.3 years), 137 underwent biopsy and 79 (31%) harboured \u2265 GG2 disease. Compared to the reference standard, the AI-DSS at the 31% cancer detection rate (CDR) would have avoided 28 biopsies while missing one \u2265 GG2 cancer. This corresponded to a 70% increase in grade selectivity (from 4.6 to 7.8), 79% increase in biopsy efficiency (from 1.4 to 2.5), and a 143% increase in selective biopsy avoidance (from 2.8 to 6.8). At the reduced CDR of 30%, grade selectivity, biopsy efficiency, and selective biopsy avoidance increased by 172%, 236%, and 475%, with four \u2265 GG2 cancers missed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>An AI-DSS that integrates clinical and advanced imaging data improves the benefit-to-harm ratio of prostate biopsy decisions in a retrospective setting. Future prospective validation as part of real-world clinical workflow is required to enable clinical implementation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Current prostate cancer diagnostic pathways result in fewer unnecessary biopsies. Can an AI decision support system (AI-DSS) further improve biopsy efficiency for detecting significant cancer?<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>An AI-DSS avoided 28 biopsies in a 252-patient cohort, increasing grade selectivity, biopsy efficiency, and selective biopsy avoidance by 70%, 79%, and 143%, respectively.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Integrating an AI-DSS into clinical workflows may further reduce unnecessary prostate biopsies and overdiagnosis of indolent disease, thus potentially improving the efficiency of the prostate cancer diagnostic pathway.<\/jats:italic>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-026-12361-6","type":"journal-article","created":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T04:14:09Z","timestamp":1771560849000},"page":"5719-5727","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["AI decision support for increasing prostate biopsy efficiency: a retrospective multicentre, multiscanner study"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4500-9714","authenticated-orcid":false,"given":"Nikita","family":"Sushentsev","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zobair","family":"Arya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jobie","family":"Budd","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amy","family":"Frary","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nadia","family":"Moreira da Silva","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mirjana","family":"Ferrer Rodriguez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul","family":"Burn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard","family":"Hindley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikhil","family":"Vasdev","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Ibrahim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alison","family":"Bradley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adrian","family":"Andreou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sidath","family":"Liyanage","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raj","family":"Persad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Aning","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander B. C. D.","family":"Ng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aqua","family":"Asif","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Veeru","family":"Kasivisvanathan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tristan","family":"Barrett","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Hinton","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anwar Roshanali","family":"Padhani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aarti","family":"Shah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lucy","family":"Davies","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antony","family":"Rix","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Evis","family":"Sala","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,20]]},"reference":[{"key":"12361_CR1","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.eururo.2024.03.027","volume":"86","author":"P Cornford","year":"2024","unstructured":"Cornford P, van den Bergh RCN, Briers E et al (2024) EAU-EANM-ESTRO-ESUR-ISUP-SIOG guidelines on prostate cancer\u20142024 update. Part I: Screening, diagnosis, and local treatment with curative intent. Eur Urol 86:148\u2013163","journal-title":"Eur Urol"},{"key":"12361_CR2","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","journal-title":"J Urol"},{"key":"12361_CR3","doi-asserted-by":"publisher","first-page":"E184","DOI":"10.5489\/cuaj.7851","volume":"16","author":"RJ Mason","year":"2022","unstructured":"Mason RJ, Marzouk K, Finelli A et al (2022) UPDATE\u20142022 Canadian Urological Association recommendations on prostate cancer screening and early diagnosis: endorsement of the 2021 Cancer Care Ontario guidelines on prostate multiparametric magnetic resonance imaging. Can Urol Assoc J 16:E184\u2013E196","journal-title":"Can Urol Assoc J"},{"key":"12361_CR4","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1038\/s41391-024-00896-y","volume":"28","author":"SJC Soerensen","year":"2024","unstructured":"Soerensen SJC, Li S, Langston ME, Fan RE, Rusu M, Sonn GA (2024) Trends in pre-biopsy MRI usage for prostate cancer detection, 2007\u20132022. Prostate Cancer Prostatic Dis 28:519\u2013522","journal-title":"Prostate Cancer Prostatic Dis"},{"key":"12361_CR5","doi-asserted-by":"publisher","unstructured":"Sushentsev N, Comune R, Sinci KA et al (2025) A 10-year analysis of MRI-driven prostate cancer diagnosis and active surveillance: trends and implications. BJU Int. https:\/\/doi.org\/10.1111\/BJU.16743","DOI":"10.1111\/BJU.16743"},{"key":"12361_CR6","doi-asserted-by":"publisher","first-page":"1683","DOI":"10.1016\/S0140-6736(24)00651-2","volume":"403","author":"ND James","year":"2024","unstructured":"James ND, Tannock I, N\u2019Dow J et al (2024) The Lancet Commission on prostate cancer: planning for the surge in cases. Lancet 403:1683\u20131722","journal-title":"Lancet"},{"key":"12361_CR7","doi-asserted-by":"publisher","unstructured":"Schoots IG, Padhani AR (2025) A paradigm shift toward population-based MRI-targeted prostate cancer screening. JAMA Oncol. https:\/\/doi.org\/10.1001\/JAMAONCOL.2025.1594","DOI":"10.1001\/JAMAONCOL.2025.1594"},{"key":"12361_CR8","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.jacr.2024.10.019","volume":"22","author":"EW Christensen","year":"2025","unstructured":"Christensen EW, Parikh JR, Drake AR, Rubin EM, Rula EY (2025) Projected US radiologist supply, 2025 to 2055. J Am Coll Radiol 22:161\u2013169","journal-title":"J Am Coll Radiol"},{"key":"12361_CR9","doi-asserted-by":"publisher","first-page":"691","DOI":"10.2214\/AJR.22.27615","volume":"219","author":"T Barrett","year":"2022","unstructured":"Barrett T, Ghafoor S, Gupta RT et al (2022) Prostate MRI qualification: AJR Expert Panel narrative review. AJR Am J Roentgenol 219:691\u2013702","journal-title":"AJR Am J Roentgenol"},{"key":"12361_CR10","doi-asserted-by":"publisher","first-page":"6168","DOI":"10.1007\/s00330-024-10626-6","volume":"34","author":"AR Padhani","year":"2024","unstructured":"Padhani AR, Godtman RA, Schoots IG (2024) Key learning on the promise and limitations of MRI in prostate cancer screening. Eur Radiol 34:6168\u20136174","journal-title":"Eur Radiol"},{"key":"12361_CR11","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1148\/radiol.2020190646","volume":"296","author":"AC Westphalen","year":"2020","unstructured":"Westphalen AC, McCulloch CE, Anaokar JM et al (2020) Variability of the positive predictive value of PI-RADS for prostate MRI across 26 centers: experience of the Society of Abdominal Radiology prostate cancer disease-focused panel. Radiology 296:76\u201384","journal-title":"Radiology"},{"key":"12361_CR12","doi-asserted-by":"publisher","unstructured":"Schoots IG, Ahmed HU, Albers P et al (2025) Magnetic resonance imaging\u2013based biopsy strategies in prostate cancer screening: a systematic review. Eur Urol. https:\/\/doi.org\/10.1016\/J.EURURO.2025.05.038","DOI":"10.1016\/J.EURURO.2025.05.038"},{"key":"12361_CR13","doi-asserted-by":"crossref","unstructured":"Giganti F, Moreira da Silva N, Yeung M et al (2025) AI-powered prostate cancer detection: a multi-centre, multi-scanner validation study. Eur Radiol 35:4915\u20134924","DOI":"10.1007\/s00330-024-11323-0"},{"key":"12361_CR14","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","journal-title":"Lancet Oncol"},{"key":"12361_CR15","doi-asserted-by":"publisher","unstructured":"Asif A, Nathan A, Ng A et al (2023) Comparing biparametric to multiparametric MRI in the diagnosis of clinically significant prostate cancer in biopsy-naive men (PRIME): a prospective, international, multicentre, non-inferiority within-patient, diagnostic yield trial protocol. BMJ Open. https:\/\/doi.org\/10.1136\/BMJOPEN-2022-070280","DOI":"10.1136\/BMJOPEN-2022-070280"},{"key":"12361_CR16","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.eururo.2019.02.033","volume":"76","author":"B Turkbey","year":"2019","unstructured":"Turkbey B, Rosenkrantz AB, Haider MA et al (2019) Prostate Imaging Reporting and Data System version 2.1: 2019 update of Prostate Imaging Reporting and Data System Version 2. Eur Urol 76:340\u2013351","journal-title":"Eur Urol"},{"key":"12361_CR17","doi-asserted-by":"crossref","unstructured":"Ng AB, Asif A, Agarwal R et al (2025) Biparametric vs Multiparametric MRI for Prostate Cancer Diagnosis: The PRIME Diagnostic Clinical Trial. JAMA. 334:1170\u20131179","DOI":"10.1001\/jama.2025.13722"},{"key":"12361_CR18","unstructured":"NICE (2019) Prostate cancer: diagnosis and management. Available via https:\/\/www.nice.org.uk\/guidance\/ng131\/chapter\/Recommendations. Accessed 4 Aug 2025"},{"key":"12361_CR19","doi-asserted-by":"publisher","unstructured":"Sushentsev N, Barrett T, Warren AY, Gnanapragasam VJ (2022) How and when should radiologists report T-staging on MRI in patients with prostate cancer? BJU Int. https:\/\/doi.org\/10.1111\/BJU.15824","DOI":"10.1111\/BJU.15824"},{"key":"12361_CR20","doi-asserted-by":"publisher","first-page":"598","DOI":"10.1111\/his.15231","volume":"85","author":"JK Nguyen","year":"2024","unstructured":"Nguyen JK, Harik LR, Klein EA et al (2024) Proposal for an optimised definition of adverse pathology (unfavourable histology) that predicts metastatic risk in prostatic adenocarcinoma independent of grade group and pathological stage. Histopathology 85:598\u2013613","journal-title":"Histopathology"},{"key":"12361_CR21","doi-asserted-by":"publisher","first-page":"1547","DOI":"10.1056\/NEJMoa2214122","volume":"388","author":"FC Hamdy","year":"2023","unstructured":"Hamdy FC, Donovan JL, Lane JA et al (2023) Fifteen-year outcomes after monitoring, surgery, or radiotherapy for prostate cancer. N Engl J Med 388:1547\u20131558","journal-title":"N Engl J Med"},{"key":"12361_CR22","doi-asserted-by":"crossref","unstructured":"Sushentsev N, Warren AY, Colling R et al (2025) Active Monitoring, Surgery, and Radiotherapy for Cribriform-Positive and Cribriform-Negative Prostate Cancer: A Secondary Analysis of the PROTECT Randomized Clinical Trial. JAMA Oncol. 11:1512\u20131517","DOI":"10.1001\/jamaoncol.2025.4125"},{"key":"12361_CR23","doi-asserted-by":"publisher","unstructured":"Sushentsev N, Li IG, Xu G et al (2025) Predicting active surveillance failure for patients with prostate cancer in the magnetic resonance imaging era: a multicentre transatlantic cohort study. Eur Urol Oncol. https:\/\/doi.org\/10.1016\/J.EUO.2025.06.012","DOI":"10.1016\/J.EUO.2025.06.012"},{"key":"12361_CR24","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1001\/jamaoncol.2024.5497","volume":"11","author":"CA Hamm","year":"2025","unstructured":"Hamm CA, Asbach P, P\u00f6hlmann A et al (2025) Oncological safety of MRI-informed biopsy decision-making in men with suspected prostate cancer. JAMA Oncol 11:145\u2013153","journal-title":"JAMA Oncol"}],"container-title":["European Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-026-12361-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00330-026-12361-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-026-12361-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T10:17:19Z","timestamp":1781864239000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00330-026-12361-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,20]]},"references-count":24,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["12361"],"URL":"https:\/\/doi.org\/10.1007\/s00330-026-12361-6","relation":{},"ISSN":["1432-1084"],"issn-type":[{"value":"1432-1084","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,20]]},"assertion":[{"value":"9 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 December 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 February 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The scientific guarantor of this publication is Evis Sala.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Guarantor"}},{"value":"The following authors declare relationships with the following companies: N.S.: consultant, Lucida Medical; Z.A., J.B., A.F., M.F.R., N.M.d.S., M.H., L.D., A.R., E.S.: employee\/consultant\/stockholder, Lucida Medical; J.A., R.P., R.H.: stockholder, Lucida Medical; N.V., A.S.: travel stipends, Lucida Medical; A.R.P.: consultant and stockholder, Lucida Medical, speakers bureau and research support, Siemens Healthineers. A.B.C.D.N. receives grants from Prostate Cancer UK outside the submitted work; V.K. receives grants from the John Black Charitable Foundation and Prostate Cancer UK, European Association of Urology Research Foundation, and Wolfgang Dieckmann Foundation and consultant to Telix Pharmaceuticals. The remaining authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"One of the authors has significant statistical expertise.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was obtained from all subjects (patients) in this study.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Data for this study came from two studies. For the PRIME study, ethical approval was obtained from the National Research Ethics Committee West Midlands, Nottingham 21\/WM\/0091 on 28th June 2021. PAIR-1 had approval from the NHS Health Research Authority (IRAS #278640). No research ethics committee (REC) approval was required due to the data being anonymised and retrospective.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Study subjects in this study came from the previously reported PAIR-1 and PRIME studies. The external validation set was the same as the PAIR-1 study, while a subset of the PRIME cohort was used for training the model here. This study reports a fundamentally different model (AI integrating clinical features as well as imaging), compared to the original PAIR-1 and PRIME studies.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}