A PI-RADS-Based New Nomogram for Predicting Clinically Significant Prostate Cancer: A Cohort Study

Area of Science:

• Urology
• Radiology
• Oncology

Background:

• Accurate prediction of clinically significant prostate cancer (csPCa) is crucial for guiding biopsy decisions.
• Existing methods may not fully integrate multiparametric MRI (mpMRI) findings with clinical risk factors for optimal prediction.

Purpose of the Study:

• To develop and validate a PI-RADS (Prostate Imaging Reporting and Data System)-based nomogram for predicting csPCa probability.
• To integrate PI-RADS scores with independent clinical risk factors for enhanced prebiopsy risk stratification.

Main Methods:

• Development and validation cohorts comprising 573 and 253 patients, respectively.
• Univariate and multivariate analyses to identify independent clinical risk factors (age, PSA density, free-to-total PSA ratio).
• Construction of a nomogram integrating PI-RADS scores and identified clinical factors; performance assessed using AUC, NRI, calibration curves, and decision curves.

Main Results:

• The developed nomogram demonstrated superior performance in predicting csPCa compared to individual factors or PI-RADS score alone in both cohorts (AUC=0.894 in development, 0.891 in validation).
• Net reclassification improvement analysis confirmed significant enhancement in patient classification.
• The nomogram exhibited favorable calibration and clinical utility.

Conclusions:

• A validated PI-RADS-based nomogram effectively integrates MRI findings with clinical variables for individualized prebiopsy prediction of csPCa.
• This tool aids clinicians in making more informed decisions for high-risk patients, potentially optimizing biopsy strategies.