Background: Almost 30% of patients with rectal cancer (RC) who submit to comprehensive treatment experience relapse. Surveillance plays a leading role in early detection. The landmark approach provides a more flexible and dynamic framework for survival prediction.Objective: This large retrospective study aims to develop a machine learning algorithm to profile the patient prognosis, especially the risk and the onset of RC relapse after curative resection.Methods: A cohort of 2450 RC patients were analyzed using landmark analysis. Model A applied a classical cause-specific Cox approach with a landmarking approach, while Model B implemented a landmarking-based RSF (random survival forest) competing risk algorithm. The two models were compared in terms of predictive and interpretative ability. A bootstrapped validation strategy was employed to validate the model’s performance and prevent overfitting. The best-performing hyperparameters were selected systematically, ensuring the model’s robustness within the landmark approach. The study assessed these factors’ importance and interactions using RSF and compared the predictive accuracy to that of the classical Cox model.Results: Model B outperformed Model A (mean C-index 0.95 vs. 0.78), capturing complex interactions and providing dynamic, individualized relapse predictions. Clinical factors influencing survival outcomes were identified across time with the landmark approach allowing for more accurate and timely predictions.Conclusions: The landmark approach offers an improvement over traditional methods in survival analysis. By accommodating time-dependent variables and the evolving nature of patient data, this approach provides a precise tool for profiling RC survival, thereby supporting more informed and dynamic clinical decision-making.
背景：约30%接受综合治疗的直肠癌患者会出现复发。监测在早期发现中起主导作用。地标分析法为生存预测提供了更灵活、动态的框架。 目的：这项大型回顾性研究旨在开发一种机器学习算法，以分析患者预后，特别是直肠癌根治性切除术后复发的风险与发生时间。 方法：采用地标分析法对2450例直肠癌患者队列进行分析。模型A采用经典病因特异性Cox回归结合地标分析法，模型B则实施基于地标分析的随机生存森林竞争风险算法。比较两种模型的预测与解释能力。采用自助法验证策略评估模型性能并防止过拟合。系统筛选最优超参数，确保模型在地标分析框架内的稳健性。研究通过随机生存森林评估各因素的重要性及交互作用，并与经典Cox模型的预测准确性进行比较。 结果：模型B表现优于模型A（平均C指数0.95对0.78），能捕捉复杂交互作用并提供动态、个体化的复发预测。通过地标分析法识别出随时间变化的临床生存影响因素，实现了更精准及时的预测。 结论：地标分析法在生存分析中较传统方法具有显著改进。通过整合时变变量和患者数据的动态演变特征，该方法为直肠癌生存分析提供了精准工具，有助于实现更科学、动态的临床决策。
肿瘤(癌症)患者之家