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存在显著异质性，给个体化管理带来挑战。现有预后方法常依赖于单一术后快照的静态表示，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，从而限制了预测准确性。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从初始患者表示出发，通过自回归残差更新生成12步轨迹快照序列，并将其整合以预测复发和生存结局。在MSKCC CRLM数据集上，DyPro在重复分层5折交叉验证中展现出强判别能力，总生存期（OS）C指数达0.755，无病生存期（DFS）C指数达0.714，OS一年AUC为0.920，OS综合布里尔分数（IBS）为0.143。DyPro提供量化风险线索，以支持辅助治疗方案制定和随访规划。