Data Sheet 1_Development and validation of a nomogram for predicting clinically significant prostate cancer using serologic indices, multiparametric magnetic resonance imaging, and sound touch elastography parameters: a retrospective study.docx

ObjectiveThis study aimed to investigate independent risk factors for clinically significant prostate cancer (csPCa) using serologic indices, multiparametric magnetic resonance imaging (mpMRI), and sound touch elastography (STE), and to develop and validate a nomogram-based prediction model using the optimal model derived from these factors.
MethodsA total of 240 patients who underwent ultrasound-guided transperineal prostate biopsy at Anqing Municipal Hospital between January 2024 and December 2024 were retrospectively enrolled. After applying exclusion criteria, 160 patients were included in the modeling cohort, which was divided into clinically significant prostate cancer (csPCa) and non-clinically significant prostate cancer (non-csPCa) groups based on pathological results. Additionally, 40 eligible patients from December 2024 to February 2025 were selected as the external validation cohort. Baseline data of the modeling cohort were collected, and independent risk factors for csPCa were identified using univariate and multivariate logistic regression analyses. The optimal model was selected by comparing with single-modal models, followed by developing a Nomogram prediction model. R language was used to plot decision curve analysis (DCA) for clinical utility evaluation, while receiver operating characteristic (ROC) curve and calibration curve were employed to assess predictive performance.
ResultsMultivariate logistic regression analysis identified The Prostate Imaging Reporting and Data System score, age, free-to-total (f/t) prostate-specific antigen (PSA), Emax, TZ-ratio (transition zone ratio), and lesion density as independent risk factors for csPCa (all P < 0.05). The combined independent risk factor model demonstrated superior predictive performance compared to single-modal models, with an area under the receiver operating characteristic curve (AUC) of 0.926, sensitivity of 88.0%, and specificity of 83.1%. A nomogram model was developed based on this optimal model. Decision curve analysis (DCA) revealed substantial clinical benefit and high usability across a wide range of threshold probabilities. Calibration curve validation showed excellent predictive accuracy, with close agreement between predicted and observed probabilities. Both internal and external validation cohorts confirmed consistent predictive performance of the model.
ConclusionThe nomogram model integrating serologic indices, multiparametric mpMRI, and STE provides a more accurate and reliable tool for diagnosing csPCa, demonstrating substantial potential for clinical translation.