Does Machine Learning Prediction of Magnetic Resonance Imaging Prostate Imaging Reporting and Data System Correlate with Target Prostate Biopsy Results?

Abstract

Predictive machine learning models showed an excellent ability to predict MRI Pi-RAD scores and discriminate between low- and high-risk scores. However, caution should be exercised, as a high percentage of negative biopsy cases were assigned Pi-RAD 4 and Pi-RAD 5 scores. ML integration may enhance PI-RAD's utility by reducing unnecessary biopsies in low-risk patients (via better csPCa detection) and refining the high-risk categorization. Combining such PI-RAD scores with significant parameters, such as PSA density, lesion diameter, number of lesions, and age, in decision curve analysis and utility paradigms would assist physicians' clinical decisions. .