MRI对术后勃起功能障碍预测帮助有限,临床信息仍最可靠。
Evaluating the Predictive Value of Preoperative MRI for Erectile Dysfunction Following Radical Prostatectomy
- 比较了临床数据、手工特征、深度学习和多模态融合四种预测方法
- 临床模型准确率最高(AUC 0.663),MRI模型最高仅0.569
- 尽管影像模型聚焦关键解剖区域,但未超越临床预测
术前准确预测根治性前列腺切除术后勃起功能障碍(ED)对患者沟通至关重要。虽然临床特征是公认预测因子,但术前MRI的附加价值尚未充分探索。本研究评估了四种建模策略在术后12个月预测ED的表现:(1) 仅基于临床的基准模型;(2) 基于人工提取的MRI解剖特征的经典模型;(3) 直接在MRI切片上训练的深度学习模型;(4) 影像与临床数据融合的多模态模型。结果显示,影像模型(最大AUC 0.569)略优于手工特征方法(AUC 0.554),但显著低于临床基准(AUC 0.663)。融合模型仅带来微弱提升(AUC 0.586),未超过临床模型。SHAP分析表明,临床特征贡献最大。最优影像模型的显著性图显示其关注解剖学合理区域,如前列腺和神经血管束。尽管影像模型未能提升预测性能,结果表明其试图捕捉相关解剖结构模式,未来有望与临床预测协同增强。
原文摘要 · Abstract (English)
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.
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