结直肠癌肝转移的动态预后预测方法
A Dynamic Prognostic Prediction Method for Colorectal Cancer Liver Metastasis
AI总结：
本文提出DyPro框架，通过残差动态演化推断术后潜在轨迹，整合多模态临床信息，提升复发和生存预测精度。
摘要：
结直肠癌肝转移（CRLM）术后复发率高且预后异质性显著，现有方法难以联合捕捉肿瘤空间分布、纵向疾病动态和多模态临床信息。本文提出DyPro深度学习框架，通过残差动态演化推断术后潜在轨迹，生成12步轨迹快照序列并整合预测复发和生存结果。在MSKCC CRLM数据集上，DyPro在重复分层5折交叉验证中表现优异，OS C-index达0.755，DFS C-index达0.714，OS AUC@1y为0.920，OS IBS为0.143。DyPro提供定量风险提示，支持辅助治疗规划和随访安排。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
