{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T16:00:15Z","timestamp":1790438415046,"version":"4.1.0"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T00:00:00Z","timestamp":1790380800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T00:00:00Z","timestamp":1790380800000},"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 compare a commercially available artificial intelligence (AI) risk-classification system with PI-RADS for detecting clinically significant prostate cancer (csPCa) on prostate MRI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>This retrospective multicentre, multivendor diagnostic accuracy study included men who underwent prostate MRI and histopathologic verification between 2014 and 2025. Index tests were DeepHealth Prostate Suite AI risk category and radiologist-assigned PI-RADS; the reference standard was International Society of Urological Pathology Grade Group \u2265\u20092. Patient-level non-inferiority testing, receiver operating characteristic analysis, lesion-level free-response receiver operating characteristic analysis, and patient-level bootstrap confidence intervals were used. Workflow support analyses modeled biopsy-avoidance strategies and AI-based risk stratification within PI-RADS 3 lesions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      After exclusions, 787 men (median age, 70 years; interquartile range, 64\u201375) were evaluated; 380 (48.3%) had csPCa. AI sensitivity was non-inferior to PI-RADS at the patient level (97.6% vs 92.6%;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001) but not at the lesion level (78.8% vs 88.8%;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.98). Patient-level area under the curve was higher for AI than PI-RADS (0.80 vs 0.77; difference, 0.032; 95% confidence interval, 0.001\u20130.064;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.043). A targeted PI-RADS 3 strategy avoided 18.9% of biopsies while maintaining 98.4% sensitivity. Among PI-RADS 3 lesions, AI upgraded 93.1% of csPCa-positive lesions and assigned a low risk to 24.1% of benign lesions.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The AI system was non-inferior for patient-level sensitivity but not lesion-level sensitivity. AI risk classification is best positioned as decision support for biopsy triage, particularly in PI-RADS 3 lesions.<\/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>Can three-tier artificial intelligence risk classification support prostate MRI biopsy triage in men while preserving detection of clinically significant prostate cancer?<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Artificial intelligence had non-inferior patient-level sensitivity and higher patient-level discrimination than PI-RADS, but lower lesion-level sensitivity at the predefined threshold<\/jats:italic>\n                      .\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Artificial intelligence risk categories can complement PI-RADS by supporting high-sensitivity biopsy triage, especially for equivocal PI-RADS 3 lesions, while radiologists retain responsibility for lesion mapping<\/jats:italic>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-026-12888-8","type":"journal-article","created":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T15:29:53Z","timestamp":1790436593000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multicentre evaluation of artificial intelligence risk classification for detection of clinically significant prostate cancer on biparametric MRI"],"prefix":"10.1007","author":[{"given":"Karsten","family":"Guenzel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maarten G.","family":"Poirot","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Almar","family":"van Loon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emanuele","family":"Messina","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martina","family":"Pecoraro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valeria","family":"Panebianco","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Princenthal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5218-6431","authenticated-orcid":false,"given":"Francesco","family":"Giganti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,26]]},"reference":[{"key":"12888_CR1","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.eururo.2019.02.009","volume":"76","author":"J Hugosson","year":"2019","unstructured":"Hugosson J, Roobol MJ, M\u00e5nsson M et al (2019) A 16-yr follow-up of the European randomized study of screening for prostate cancer. 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F.G. reports consulting fees from DeepHealth, SpectraCure, and Procept outside of the submitted work, and speaker fees from Bayer and Siemens. He is not an employee of DeepHealth and was not involved in the conceptualisation or development of the commercial product evaluated in this study. V.P. and F.G. are members of the Scientific Editorial Board of\n                      European Radiology\n                      (section: Urogenital) and, as such, did not participate in the selection or review processes for this article. K.G., E.M., M.P. and R.P. declare no conflicts of interest related to this work.","order":2,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"One of the authors has significant statistical expertise.","order":3,"name":"Ethics","label":"Statistics and biometry","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"At the participating institutions, informed consent was either waived by the local IRB or obtained at the time of MRI examination through institutional procedures allowing future use of clinical data for research purposes.","order":4,"name":"Ethics","label":"Informed consent","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Institutional Review Board approval was obtained.","order":5,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Portions of the Vivantes and St. Antonius cohorts have been described previously: Van den Berg et al [\n                      \n                      ], Guenzel et al [\n                      \n                      ]. The present study addresses a different question by evaluating commercial AI risk classification for csPCa detection in a pooled multicentre, multivendor cohort, including patient-level non-inferiority and ROC analyses, lesion-level FROC analysis, and modeled biopsy-triage strategies. None of the imaging or pathology data in the present study were used to develop or train the evaluated AI system.","order":6,"name":"Ethics","label":"Study subjects or cohorts overlap","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"order":7,"name":"Ethics","label":"Methodology","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}}]}}