{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T19:17:52Z","timestamp":1770491872635,"version":"3.49.0"},"reference-count":23,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T00:00:00Z","timestamp":1764633600000},"content-version":"vor","delay-in-days":1,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["bjui-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BJUI Compass"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The integration of blood\u2010based biomarkers and multiparametric magnetic resonance imaging (mpMRI) has been proposed to improve prostate cancer (PCa) diagnosis. However, few validated models combine both tools to support risk\u2010adapted clinical decision\u2010making.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>The study's aim is to evaluate and internally validate a multivariable model integrating clinical, analytical and imaging parameters\u2014including the Prostate Health Index (PHI) and mpMRI\u2014for predicting clinically significant prostate cancer (csPCa) in biopsy\u2010na\u00efve men.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design, setting and participants<\/jats:title>\n                    <jats:p>This prospective observational study included 183 biopsy\u2010na\u00efve men aged 50\u201375\u2009years with PSA levels of 4\u201310\u00a0ng\/mL and\/or abnormal digital rectal examination. All patients underwent PHI testing, and 47.5% received prebiopsy mpMRI. All underwent systematic biopsy; targeted cognitive fusion biopsy was performed for PIRADS \u2265 3 lesions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Outcome measurements and statistical analysis<\/jats:title>\n                    <jats:p>A multivariable logistic regression model was constructed using PHI, PSA density, PSA free\/total ratio, PIRADS score and age. The model was internally validated with bootstrap resampling and converted into a clinical nomogram. Diagnostic accuracy (AUC, sensitivity, specificity, NPV and PPV) was assessed and compared with simplified strategies using PHI or PIRADS alone, as well as a sequential approach (PHI \u2192 PIRADS).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results and limitations<\/jats:title>\n                    <jats:p>The model achieved an AUC of 0.841 (95% CI 0.76\u20130.91), with 100% sensitivity and 66.7% specificity for csPCa in the mpMRI cohort at the optimal 17% risk threshold (65.5 points). It safely avoided 49.4% of biopsies without missing any csPCa cases. Simpler strategies using PHI or PIRADS alone showed lower efficiency, particularly in balancing sensitivity and biopsy reduction. As an additional analysis, the PHI\u2013mpMRI nomogram by Siddiqui et al. (2023) was externally validated in our cohort, confirming robust diagnostic accuracy (AUC 0.89, 95% CI 0.82\u20130.95). Limitations include the modest size of the mpMRI cohort and the historical nature of recruitment (2014\u20132018), although PHI and mpMRI remain standard in contemporary practice.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This model accurately predicts csPCa and outperforms individual tools such as PHI or PIRADS alone. Its application may improve diagnostic efficiency and reduce unnecessary procedures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Patient summary<\/jats:title>\n                    <jats:p>A model combining a blood test (PHI) and MRI can help avoid unnecessary prostate biopsies while reliably detecting aggressive cancers.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/bco2.70101","type":"journal-article","created":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T03:43:59Z","timestamp":1764733439000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multivariable model integrating PHI and mpMRI for detecting csPCa in biopsy\u2010na\u00efve men"],"prefix":"10.1002","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3590-163X","authenticated-orcid":false,"given":"Mario 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