Machine learning effectively predicts early recurrence in gastric cancer.

A multicenter study identified early recurrence (ER) within two years post-surgery in 15% of 11,615 gastric cancer patients. Utilizing machine learning, researchers developed a stacking ensemble model that achieved an area under the receiver operating characteristic curve of 1.0 for training and 0.8 for testing, indicating robust predictive capability. Key predictors included tumor size, staging, and specific invasion types, leading to the creation of an online tool for clinical decision-making.