DU Nan 1 , MA Wenxiang 1 , XIE Jiajing 1 , YANG Yang 1 , YU Yongjiang 2
  • 1. The First Clinical Medical College of Lanzhou University, Lanzhou 730000, P. R. China;
  • 2. Department of Gastrointestinal Surgery, Hernia and Abdominal Wall Surgery, The First Hospital of Lanzhou University, Lanzhou 730000, P. R. China;
YU Yongjiang, Email: ylongy@163.com
gastric cancer; anastomotic leakage; complications; risk factors; prediction model
10.7507/1007-9424.202504071
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Objective To systematically summarize the research progress in risk prediction models for postoperative anastomotic leakage (AL) in gastric cancer, and to explore the advantages and limitations of models constructed using traditional statistical methods and machine learning (ML), thereby providing a theoretical basis for clinical precision prediction and early intervention. Method By analyzing domestic and international literature, the construction strategies of logistic regression, LASSO regression, and ML models (support vector machine, random forest, deep learning) were systematically reviewed, and their predictive performance and clinical applicability were compared. Results The traditional logistic regression and LASSO regression models performed excellently in terms of interpretability and in small-sample scenarios but were limited by linear assumptions. The ML models significantly enhanced predictive capabilities for complex data through non-linear modeling and automatic feature extraction, but required larger data scales and had higher demands for interpretability. Conclusions Different prediction models have their own advantages and limitations; in practical clinical applications, they should be flexibly selected or complementarily applied based on specific scenarios. Current AL prediction models are evolving from single-factor analysis to multi-modal dynamic integration. Future efforts should combine artificial intelligence, multi-center prospective validation, and clinical studies to advance the development of precise and individualized predictive tools for patients.

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