Evaluating the Predictive Value of Preoperative MRI for Erectile Dysfunction Following Radical Prostatectomy
Publication date
2026
Editors
Rekik, Islem
Adeli, Ehsan
Park, Sang Hyun
Cintas, Celia
Advisors
Supervisors
Document Type
Part of book
Metadata
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License
taverne
Abstract
Accurate preoperative prediction of erectile dysfunction (ED) is important for counseling patients undergoing radical prostatectomy. While clinical features are well-established predictors, the added value of preoperative MRI remains underexplored. We assess whether MRI provides additional predictive value for ED at 12 months post-surgery, evaluating four strategies: (1) a clinical-only baseline; (2) classical models using handcrafted MRI-based features; (3) deep learning models trained on MRI slices; and (4) fusion of imaging and clinical inputs. Imaging-based models (AUC 0.569) slightly outperformed handcrafted 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 surpass clinical-only performance. SHAP analysis on the fusion method confirmed that clinical features were the primary drivers of predictive accuracy, while saliency maps suggested that imaging models focused on anatomically relevant regions. However, MRI offered limited added value beyond clinical data in prediction, likely due to dataset size, imaging-related variability, and missing clinical confounders. Larger, standardized datasets and improved integration strategies are needed to better evaluate MRI’s potential in ED prediction.
Keywords
Deep Learning, Erectile Dysfunction, Medical Imaging, MRI, Prostate Cancer, Prostatectomy, Taverne, Theoretical Computer Science, General Computer Science, SDG 3 - Good Health and Well-being
Citation
Rouwendaal, G N L, Boeke, D, Cox, I L, van der Poel, H G, van Dijk-de Haan, M C, Beets-Tan, R G H, Boellaard, T N & Silva, W 2026, Evaluating the Predictive Value of Preoperative MRI for Erectile Dysfunction Following Radical Prostatectomy. in I Rekik, E Adeli, S H Park & C Cintas (eds), Predictive Intelligence in Medicine - 8th International Workshop, PRIME 2025, Held in Conjunction with MICCAI 2025, Proceedings. Lecture Notes in Computer Science, vol. 16164 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 151-162, 8th International Workshop on Predictive Intelligence in Medicine, PRIME 2025, held in conjunction with the 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, Daejeon, Korea, Republic of, 27/09/25. https://doi.org/10.1007/978-3-032-07904-6_14, conference