Evaluating the Predictive Value of Preoperative MRI for Erectile Dysfunction Following Radical Prostatectomy

TL;DR

This study assesses whether preoperative MRI adds predictive value for erectile dysfunction after prostatectomy, finding that clinical features outperform MRI-based models, but imaging may still provide complementary insights.

Contribution

It compares multiple modeling strategies, including deep learning and multimodal fusion, to evaluate MRI's role in predicting post-surgical erectile dysfunction.

Findings

MRI models slightly outperform anatomical features

Clinical features outperform MRI-based models

Fusion models offer marginal improvements

Abstract

Accurate preoperative prediction of erectile dysfunction (ED) is important for counseling patients undergoing radical prostatectomy. While clinical features are established predictors, the added value of preoperative MRI remains underexplored. We investigate whether MRI provides additional predictive value for ED at 12 months post-surgery, evaluating four modeling strategies: (1) a clinical-only baseline, representing current state-of-the-art; (2) classical models using handcrafted anatomical features derived from MRI; (3) deep learning models trained directly on MRI slices; and (4) multimodal fusion of imaging and clinical inputs. Imaging-based models (maximum AUC 0.569) slightly outperformed handcrafted anatomical approaches (AUC 0.554) but fell short of the clinical baseline (AUC 0.663). Fusion models offered marginal gains (AUC 0.586) but did not exceed clinical-only performance. SHAP analysis confirmed that clinical features contributed most to predictive performance. Saliency maps from the best-performing imaging model suggested a predominant focus on anatomically plausible regions, such as the prostate and neurovascular bundles. While MRI-based models did not improve predictive performance over clinical features, our findings suggest that they try to capture patterns in relevant anatomical structures and may complement clinical predictors in future multimodal approaches.