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

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: AUC = 0.92

• Primary Outcome: AUC for predicting ISUP grade metastatic risk groups

• Nnt Or Sensitivity: Sensitivity and specificity not reported despite optimal threshold identification

• Confidence Interval: Not reported

Clinical Application

Requires DCE-MRI capability and machine learning infrastructure, which may limit implementation in smaller centres Would need validation in Australian population. DCE-MRI availability varies across public and private sectors. Integration with existing PSA-based screening protocols would require RACGP guideline consideration. Men with suspected or confirmed prostate cancer requiring risk stratification

Abstract

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.

References

1. 1.Panc, K., Sekmen, S., Gundogdu, H., Basaran, M., Bulbul, H. M., & Gurun, E. (2026). Integrative machine learning model leveraging DCE-MRI and PSA values for advanced risk stratification in prostate cancer. Journal of Clinical Ultrasound, Article jcu.70129. https://doi.org/10.1002/jcu.70129