{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T06:06:50Z","timestamp":1791526010925,"version":"4.3.4"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T00:00:00Z","timestamp":1740700800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T00:00:00Z","timestamp":1740700800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Lucida Medical"}],"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>Multi-centre, multi-vendor validation of artificial intelligence (AI) software to detect clinically significant prostate cancer (PCa) using multiparametric magnetic resonance imaging (MRI) is lacking. We compared a new AI solution, validated on a separate dataset from different UK hospitals, to the original multidisciplinary team (MDT)-supported radiologist\u2019s interpretations.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Materials and methods<\/jats:title>\n            <jats:p>A Conformit\u00e9 Europ\u00e9enne (CE)-marked deep-learning (DL) computer-aided detection (CAD) medical device (Pi) was trained to detect Gleason Grade Group (GG)\u2009\u2265\u20092 cancer using retrospective data from the PROSTATEx dataset and five UK hospitals (793 patients). Our separate validation dataset was on six machines from two manufacturers across six sites (252 patients). Data included in the study were from MRI scans performed between August 2018 to October 2022. Patients with a negative MRI who did not undergo biopsy were assumed to be negative (90.4% had prostate-specific antigen density\u2009&lt;\u20090.15\u2009ng\/mL<jats:sup>2<\/jats:sup>). ROC analysis was used to compare radiologists who used a 5-category suspicion score.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>GG\u2009\u2265\u20092 prevalence in the validation set was 31%. Evaluated per patient, Pi was non-inferior to radiologists (considering a 10% performance difference as acceptable), with an area under the curve (AUC) of 0.91 vs. 0.95. At the predetermined risk threshold of 3.5, the AI software\u2019s sensitivity was 95% and specificity 67%, while radiologists at Prostate Imaging-Reporting and Data Systems\/Likert \u2265\u20093 identified GG\u2009\u2265\u20092 with a sensitivity of 99% and specificity of 73%. AI performed well per-site (AUC\u2009\u2265\u20090.83) at the patient-level independent of scanner age and field strength.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>Real-world data testing suggests that Pi matches the performance of MDT-supported radiologists in GG\u2009\u2265\u20092 PCa detection and generalises to multiple sites, scanner vendors, and models.<\/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>The performance of artificial intelligence-based medical tools for prostate MRI has yet to be evaluated on multi-centre, multi-vendor data to assess generalisability.<\/jats:italic>\n            <\/jats:p>\n            <jats:p>\n              <jats:bold>\n                <jats:italic>Findings<\/jats:italic>\n              <\/jats:bold>\n              <jats:italic>A dedicated AI medical tool matches the performance of multidisciplinary team-supported radiologists in prostate cancer detection and generalises to multiple sites and scanners.<\/jats:italic>\n            <\/jats:p>\n            <jats:p>\n              <jats:bold>\n                <jats:italic>Clinical relevance<\/jats:italic>\n              <\/jats:bold>\n              <jats:italic>This software has the potential to support the MRI process for biopsy decision-making and target identification, but future prospective studies, where lesions identified by artificial intelligence are biopsied separately, are needed.<\/jats:italic>\n            <\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Graphical Abstract<\/jats:title>\n          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They are not employees of the company and have no financial relationship with Lucida beyond receiving consulting fees for their clinical expertise and contributions to this manuscript. Additionally, they were not involved in the conceptualisation or development of the commercial product. The following authors declare relationships with the following companies: F.G., N.S., N.A., G.B., I.C., R.W. consultant, Lucida Medical; N.M.d.S, M.Y., L.D., A.F., M.F.R., C.W., J.B., Z.A., S.B., M.H., 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. F.G. reports consulting fees for SpectraCure outside of the submitted work, and he has received speaker fees from Bayer; J.S. is affiliated with Fabrica AI Corp.","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":"The NHS Health Research Authority confirmed that informed consent was not required due to the data being anonymised and retrospective.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Approval was obtained from the NHS Health Research Authority (IRAS #278640). The HRA confirmed 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":"N\/A","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}