From Traditional Risk Factors to Machine Learning Models: Advancing the Prediction of Anastomotic Leak and Other Major Complications in Colorectal Cancer Surgery

Abstract

Background: Colorectal cancer (CRC) represents a major global health burden, accounting for roughly 10% of all newly diagnosed cancers and cancer-related deaths worldwide. According to the World Health Organization, it is the third most diagnosed malignancy and the second leading cause of cancer mortality. Postoperative complications remain a significant concern after CRC resection, occurring in up to 50% of patients and contributing to increased morbidity, mortality, prolonged hospitalization, and substantial healthcare expenditure. Artificial intelligence (AI) has emerged as a transformative tool in modern healthcare, offering advanced capabilities in predictive analytics, clinical decision support, and personalized perioperative management. Methods: This review systematically evaluates the application of AI, specifically machine learning (ML) and deep learning (DL) algorithms, in the prediction of anastomotic leak (AL) and other major postoperative complications. In this context, AI models are generally used to refine risk stratification and enhance surgical decision-making. Results: A total of 13 studies were included, encompassing 15,105 patients. Across these studies, ML and DL algorithms consistently outperformed conventional statistical models in forecasting postoperative outcomes. Conclussions: Current evidence suggests that AI has substantial potential to improve perioperative risk prediction, support intraoperative decision-making, and personalize postoperative surveillance in patients undergoing CRC surgery. Methodological limitations, including a high risk of bias, limited external validation, heterogeneous outcome definitions, and inconsistent reporting, necessitate more robust, prospective, multicenter research before widespread clinical adoption can be realized.

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