Developing a diagnostic model for predicting prostate cancer: a retrospective study based on Chinese multicenter...

Area of Science:

• Urology
• Oncology
• Medical Diagnostics

Background:

• Overdiagnosis of prostate cancer (PCa) due to elevated PSA is a global issue.
• Overtreatment of indolent PCa necessitates improved diagnostic strategies.

Purpose of the Study:

• To develop a prediction model for clinically significant prostate cancer (csPCa).
• To establish a risk stratification system to minimize unnecessary prostate biopsies.

Main Methods:

• Retrospective study of 1807 patients from three Chinese hospitals.
• Stepwise logistic regression analysis to build the final model.
• Validation using receiver operating characteristic curves, calibration plots, and decision curve analysis.

Main Results:

• A diagnostic model combining PSA density and PI-RADS score was established.
• The model demonstrated excellent discrimination and calibration in development and external cohorts.
• A risk stratification system with thresholds of 0.05 and 0.60 was created.
• Low-risk patients (threshold <0.05) showed high csPCa-free survival rates (99.7% at 12 months, 99.4% at 24 months).

Conclusions:

• The developed diagnostic model and risk stratification system enable personalized csPCa risk calculation.
• This provides a standardized tool for Chinese patients and physicians to guide prostate biopsy decisions.