AUTHOR=Lin Zhenmeng , Yan Mingfang , Chen Hai , Wei Shenghong , Li Yangming , Jian Jinliang 

TITLE=Development and validation of a machine learning model to predict postoperative complications following radical gastrectomy for gastric cancer

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1606938

DOI=10.3389/fonc.2025.1606938

ISSN=2234-943X

ABSTRACT=ObjectivePostoperative complications significantly adversely affect recovery and prognosis following radical gastrectomy for gastric cancer. We developed and validated machine learning (ML) models to predict these complications and constructed a clinically applicable dynamic nomogram.MethodsUsing a prospectively maintained database, we conducted a retrospective analysis of 1,486 patients from Fujian Cancer Hospital (training cohort) and 498 from the First Hospital of Putian City (validation cohort). Feature selection integrated Lasso regression, the Boruta algorithm, and Recursive Feature Elimination (RFE). Six ML models were developed and evaluated: TreeBagger (TB), Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Gaussian Naïve Bayes (GNB), and Artificial Neural Network (ANN). The significant predictors identified were incorporated into a logistic regression model to determine independent risk factors, which then formed the basis of a dynamic nomogram deployed as an interactive web application for clinical use.ResultsRF demonstrated numerically superior performance among the evaluated models in both cohorts. Independent risk factors included age, BMI, diabetes mellitus, ASA grade, operative time, and surgical approach. The dynamic nomogram achieved AUCs of 0.805 (training) and 0.856 (validation), with calibration curves and decision curve analysis confirming its reliability. DeLong’s test revealed no significant difference in AUC between the RF model and nomogram in either cohort (training: Z = -0.385, p = 0.701; validation: Z = -1.756, p = 0.058).ConclusionWhile the RF model provided optimal predictive accuracy among ML algorithms, the interpretable nomogram offers comparable discrimination and clinical accessibility. Both tools facilitate the early identification of high-risk patients, enabling personalized interventions to optimize postoperative recovery.