| Sumario: | Simple Summary: Rectal cancer relapse after curative surgery usually results in poorer survival and quality of life, so early identification of patients at high risk of relapse from rectal tumors is crucial. It is essential to offer risk-based recommendations that address the unique needs of survivors and caregivers while reducing the impact of provider shortages and managing costs for healthcare systems, survivors, and families. This large retrospective study aims to develop a machine learning algorithm using data from the RALAR study for profiling the risk and the onset of rectal cancer relapse after curative resection. Specifically, we proposed a machine learning algorithm that could assist clinicians in predicting patient prognosis by minimizing late relapse diagnosis, consequent delayed treatment, and the inefficient use of economic resources. 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.
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