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mpMRI-based interpretable machine learning model for predicting castration-resistant prostate cancer risk.

Siang Shen1, Bitian Liu2, Yutong Li1

  • 1Department of Nuclear Medine, Shengjing Hospital of China Medical University, Shenyang, China.

European Journal of Radiology
|November 25, 2025
PubMed
Summary
This summary is machine-generated.

This study developed a machine learning model using multiparametric MRI (mpMRI) to predict Castration-Resistant Prostate Cancer (CRPC) progression risk in advanced prostate cancer (PCa) patients on androgen deprivation therapy (ADT). The stacking model showed strong predictive capability, aiding in risk-stratified interventions.

Keywords:
Androgen deprivation therapyCastration-resistant prostate cancerMachine learningMultiparametric magnetic resonance imagingProstate cancer

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Area of Science:

  • Oncology
  • Radiology
  • Machine Learning

Background:

  • Androgen deprivation therapy (ADT) efficacy varies significantly among advanced prostate cancer (PCa) patients.
  • Predicting the progression to Castration-Resistant Prostate Cancer (CRPC) is crucial for treatment planning.

Purpose of the Study:

  • To develop and validate a machine learning model using multiparametric MRI (mpMRI) data to predict CRPC progression risk.
  • To improve risk stratification for advanced PCa patients undergoing ADT.

Main Methods:

  • Retrospective analysis of 180 advanced PCa patients who underwent mpMRI before ADT.
  • Radiomic and clinical features were extracted and analyzed using various machine learning algorithms, including a stacking ensemble model.
  • Model performance was evaluated using AUC, accuracy, precision, recall, and F1-score, with SHapley Additive exPlanations for interpretability.

Main Results:

  • The combined mpMRI-clinical model (AUC: 0.84) outperformed mpMRI-alone.
  • The stacking ensemble model achieved high predictive accuracy for CRPC progression (AUC: 0.89 internal, 0.82 external).
  • The stacking model showed strongest discriminative capability in the low-risk group (AUC: 0.89).

Conclusions:

  • The developed stacking model demonstrates significant potential for predicting CRPC progression risk in advanced PCa.
  • This model can facilitate clinically actionable risk-stratified interventions for improved patient management.