Integrative Machine Learning Model Leveraging DCE-MRI and PSA Values for Advanced Risk Stratification in Prostate Cancer.

PURPOSE: Accurate grading of prostate cancer is critical for treatment strategies and risk stratification. This study aims to develop a machine learning (ML) model integrating Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) pharmacokinetic parameters with Prostate-Specific Antigen (PSA) values to predict ISUP grade metastatic risk groups.

METHODS

This retrospective study included 102 patients with histologically confirmed prostate cancer. DCE-MRI pharmacokinetic parameters (Ktrans, Kep, Ve, CER, MaxSlope, IAUGC) were standardized. The dataset was balanced using the Synthetic Minority Oversampling Technique and split into training, validation, and test sets. ML models, including Random Forest, were evaluated using Area Under the Curve (AUC) values.

RESULTS

The Random Forest classifier achieved the highest performance, with an AUC of 0.92. Precision-recall analysis identified an optimal threshold of 0.3, balancing sensitivity and specificity for high-risk group detection. SHAP analysis highlighted PSA, MaxSlope, and Kep as key predictors contributing to model accuracy.

CONCLUSION

Integrating DCE-MRI parameters with PSA values using ML algorithms enhances the prediction of ISUP grade metastatic risk groups. This method provides a robust tool for metastasis screening and personalized treatment in prostate cancer.