通过动态轨迹建模提升结直肠癌肝转移术后复发预测精度
A Dynamic Prognostic Prediction Method for Colorectal Cancer Liver Metastasis
- 基于残差动态演化生成术后12步潜在病程轨迹
- 在MSKCC数据集上实现OS C-index 0.755，1年生存率AUC达0.920
- 为辅助治疗和随访计划提供量化风险依据
结直肠癌肝转移（CRLM）具有高术后复发率和显著的预后异质性，难以实现个体化管理。现有预后方法多依赖单一术后时间点的静态表征，未能联合捕捉肿瘤空间分布、纵向疾病动态及多模态临床信息，限制了预测准确性。本文提出DyPro，一种深度学习框架，通过残差动态演化推断术后潜在轨迹。从初始患者表征出发，DyPro经自回归残差更新生成12步轨迹快照序列，并融合生成结果以预测复发与生存结局。在MSKCC CRLM数据集上，经重复分层5折交叉验证，DyPro在总生存（OS）方面达到C-index 0.755，无病生存（DFS）为0.714；OS 1年AUC为0.920，IBS为0.143。该模型可为辅助治疗规划与随访安排提供量化风险提示。
原文摘要 · Abstract (English)
Colorectal cancer liver metastasis (CRLM) exhibits high postoperative recurrence and pronounced prognostic heterogeneity, challenging individualized management. Existing prognostic approaches often rely on static representations from a single postoperative snapshot, and fail to jointly capture tumor spatial distribution, longitudinal disease dynamics, and multimodal clinical information, limiting predictive accuracy. We propose DyPro, a deep learning framework that infers postoperative latent trajectories via residual dynamic evolution. Starting from an initial patient representation, DyPro generates a 12-step sequence of trajectory snapshots through autoregressive residual updates and integrates them to predict recurrence and survival outcomes. On the MSKCC CRLM dataset, DyPro achieves strong discrimination under repeated stratified 5-fold cross-validation, reaching a C-index of 0.755 for OS and 0.714 for DFS, with OS AUC@1y of 0.920 and OS IBS of 0.143. DyPro provides quantitative risk cues to support adjuvant therapy planning and follow-up scheduling.
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