This study aimed to construct nomograms for predicting the likelihood of clinically significant prostate cancer (csPCa) in patients with lesions rated as Prostate Imaging Reporting and Data System (PI-RADS) 3 on biparametric magnetic resonance imaging (bpMRI). We retrospectively analyzed a cohort of 457 patients from the Peking Union Medical College Hospital (January 2017-July 2021) to develop the model and externally validated it with a cohort of 238 patients from the Second Hospital of Tianjin Medical University (September 2017-September 2021). Univariate and multivariate logistic regression analyses identified significant predictors of csPCa, defined by tumor volumes ≥ 0.5 cm3, Gleason score ≥ 7, or presence of extracapsular extension. Diagnostic performance for the peripheral zone (PZ) and transitional zone (TZ) was compared using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Through univariate and multivariate logistic regression analyses, we identified age, prostate-specific antigen (PSA), and prostate volume (PV) as predictors of csPCa for the PZ, and age, serum-free to total PSA ratio (f/t PSA), and PSA density (PSAD) for the TZ. The nomograms demonstrated robust discriminative ability, with an area under the ROC curve (AUC) of 0.819 for PZ and 0.804 for TZ. The external validation corroborated the model's high predictive accuracy (AUC of 0.831 for PZ and 0.773 for TZ). Calibration curves indicated excellent agreement between predicted and observed outcomes, and DCA underscored the nomogram's clinical utility for both PZ and TZ. Overall, the nomograms offer high predictive accuracy for csPCa at initial biopsy, potentially reducing unnecessary biopsies in clinical settings.
Keywords: Prostatic neoplasms; magnetic resonance imaging; nomogram; predictive model; prostate biopsy.
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