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.

翻译：结直肠癌肝转移（CRLM）术后复发率高且预后异质性显著，对个体化管理构成挑战。现有预后方法通常依赖于术后单一时点的静态表征，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。我们提出DyPro，一种通过残差动态演化推断术后潜在轨迹的深度学习框架。DyPro从初始患者表征出发，通过自回归残差更新生成一个12步的轨迹快照序列，并整合这些快照以预测复发和生存结局。在MSKCC CRLM数据集上，DyPro在重复分层5折交叉验证下展现出强大的区分能力，总生存期（OS）的C指数达到0.755，无病生存期（DFS）的C指数达到0.714，其中OS的一年期曲线下面积（AUC@1y）为0.920，OS的综合Brier评分（IBS）为0.143。DyPro为辅助治疗规划和随访安排提供了定量的风险提示。