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For each case, the index-lesion PI-RADS score, systematic\u2009\u00b1\u2009targeted biopsy results, and six additional clinical variables were collected. Prostate biopsy samples served as the reference standard, defining sPCa as Gleason Grade \u2265\u20097. Handcrafted radiomic features were extracted from automatically segmented prostate glands. Four machine learning models were developed: (1) Radiomics, (2) PI-RADS, (3) PI-RADS\u2009+\u2009Radiomics, and (4) PI-RADS\u2009+\u2009Radiomics\u2009+\u2009Clinical Variables. Model performance and comparisons were evaluated using the area under the curve (AUC), while clinical utility was assessed through the decision curve analysis plot, Clinical Utility plot, and the number of avoided biopsies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The radiomics model did not perform significantly better than PI-RADS in the validation cohort (AUC 0.838 vs. 0.833,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.874). The combination of radiomics, PI-RADS, and clinical variables achieved the highest performance, with an AUC of 0.891 (95% CI: 0.853\u20130.930), significantly outperforming the other models (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.05). It also showed the highest specificity (29.41%) and biopsy avoidance rate (18.15%), although the differences were not statistically significant (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.313).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Incorporating radiomics and clinical variables into PI-RADS enhances its ability to discriminate sPCa, potentially decreasing false positives and unnecessary biopsies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Critical relevance statement<\/jats:title>\n                    <jats:p>The incorporation of clinical variables and radiomics into PI-RADS enhances its ability to predict significant prostate cancer, helping mitigate some of PI-RADS\u2019s current limitations, such as a significant false-positive rate, and might help reduce unnecessary biopsies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>PI-RADS limitations result in overdiagnosis of indolent prostatic lesions and unnecessary biopsies.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Radiomics and clinical variables enhance PI-RADS ability to detect significant prostate cancer.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Combined clinical-radiological models reduce false positives and help avoid unnecessary biopsies.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n          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