Construction of a clinically significant prostate cancer risk prediction model based on traditional diagnostic methods.

Objectives: to construct a prediction model for clinically significant prostate cancer (csPCa) based on prostate-specific antigen (PSA) levels, digital rectal examination (DRE), and transrectal ultrasonography (TRUS). Methods: We retrospectively analysed 1196 Asian patients who underwent transrectal ultrasound-guided biopsy (TRUSB) between June 2000 and February 2023. Patients were randomly divided into a training set of 837 cases (70%) and a validation set of 359 patients (30%). A csPCa risk prediction model was established using the logistic regression. The performance of the model was examined based on calibration curves, receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and clinical impact curves (CIC). Results: Serum PSA levels, age, DRE results, prostatic shape, prostatic border and hypoechoic area were associated with pathological outcomes. The area under the ROC curve of the training set was 0.890 (95%CI: 0.865-0.816). The optimal cut-off value was 0.279. The calibration curves indicated good calibration, and the DCA and CIC results demonstrated good clinical utility. Significantly, the prediction model has higher negative predictive value (89.8%) and positive predictive value (68.0%) compared with MRI. Subsequently, we developed an online calculator (https://jiwentong0.shinyapps.io/dynnomapp/) with six variables for biopsy optimization. Conclusion: This study incorporated the results of three traditional diagnostic methods to establish a cost-effective and highly accurate model for predicting csPCa before biopsy. With this model, we aim to provide a non-invasive and cost-effective tool for csPCa detection in Asia and other underdeveloped areas.