融合临床、影像与病理数据，提升前列腺癌复发预测准确率
PROFUSEme: PROstate Cancer Biochemical Recurrence Prediction via FUSEd Multi-modal Embeddings
- 通过中间融合策略整合多模态数据学习跨模态关联
- 内部验证集C指数达0.861，外部测试集达0.7107
- 适合临床辅助决策与个性化随访规划的医生和研究者
约30%接受根治性前列腺切除术（RP）的前列腺癌（PCa）患者出现生化复发（BCR），表现为前列腺特异性抗原（PSA）升高，与死亡率增加相关。在手术时即实现对BCR的早期精准预测，有助于及时调整临床决策并改善患者预后。本文提出一种基于多模态嵌入融合的前列腺癌生化复发预测方法（PROFUSEme），通过中间融合配置结合Cox比例风险回归模型，学习临床、影像与病理数据间的跨模态交互关系。定量评估显示，该方法优于后期融合策略，在内部5折嵌套交叉验证框架下平均C指数达0.861（σ=0.112），在CHIMERA 2025挑战赛预留数据集上达到C指数0.7107。
原文摘要 · Abstract (English)
Almost 30% of prostate cancer (PCa) patients undergoing radical prostatectomy (RP) experience biochemical recurrence (BCR), characterized by increased prostate specific antigen (PSA) and associated with increased mortality. Accurate early prediction of BCR, at the time of RP, would contribute to prompt adaptive clinical decision-making and improved patient outcomes. In this work, we propose prostate cancer BCR prediction via fused multi-modal embeddings (PROFUSEme), which learns cross-modal interactions of clinical, radiology, and pathology data, following an intermediate fusion configuration in combination with Cox Proportional Hazard regressors. Quantitative evaluation of our proposed approach reveals superior performance, when compared with late fusion configurations, yielding a mean C-index of 0.861 ($σ=0.112$) on the internal 5-fold nested cross-validation framework, and a C-index of 0.7107 on the hold out data of CHIMERA 2025 challenge validation leaderboard.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。