Machine learning model predicts anastomotic strictures post-surgery

A novel machine learning model demonstrates the potential to predict anastomotic strictures in patients following esophageal cancer surgery. Analyzing data from 1,549 patients, the gradient boosting machine achieved an area under the curve (AUC) of 0.886 in the training set and 0.872 in the validation set. Key predictive variables included anastomotic leakage, suture method, and neoadjuvant therapy. This model may enhance early detection and management of complications, ultimately improving patient outcomes.