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

OBJECTIVES

This study aimed to predict and classify magnetic resonance imaging (MRI) Prostate Imaging Reporting and Data System (PI-RADS) scores using different machine learning algorithms and to detect the concordance of PI-RADS scoring with the outcome target of prostate biopsy.

METHODS

Machine learning (ML) algorithms were used to develop best-fitting models for the prediction and classification of MRI PI-RAD. The Random Forest and Extra Trees models achieved the best performance compared to the other methods.

RESULTS

The accuracy of both models was 91.95%. The AUC was 0.9329 for the Random Forest model and 0.9404 for the Extra Trees model. PSA level, PSA density, and diameter of the largest lesion were the most important features for the importance of outcome classification. ML prediction enhanced the PI-RAD classification, where clinically significant prostate cancer (csPCa) cases increased from 0% to 1.9% in the low-risk PI-RAD class, this showed that the model identified some previously missed cases.

CONCLUSIONS

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.