A Novel Predictive Model for Anastomotic Leakage in Colorectal Cancer Using Auto-artificial Intelligence.

Abstract

Anastomotic leakage (AL) in left-sided colorectal cancer is a serious complication, with an incidence rate of 6-18%. We developed a novel predictive model for AL in colorectal surgery with double-stapling technique (DST) anastomosis using auto-artificial intelligence (AI). A total of 256 patients who underwent curative surgery for left-sided colorectal cancer between 2017 and 2021 were included. In addition to conventional clinicopathological factors, we included the type of circular stapler using DST, conventional double-row circular stapler (DCS) or EEA™ circular stapler with Tri-Staple™ technology, 28 mm Medium/Thick (Covidien, New Haven, CT, USA) which had triple-row circular stapler (TCS) as a covariate. Auto-AI software Prediction One (Sony Network Communications Inc.) was used to predict AL with 5-fold cross validation. Predictive accuracy was assessed using the area under the receiver operating characteristic curve. Prediction One also evaluated the 'importance of variables' (IOV) using a method based on permutation feature importance. The area under the curve of the AI model was 0.766. The type of circular stapler used was the most influential factor contributing to AL (IOV=0.551). This auto-AI predictive model demonstrated an improvement in accuracy compared to the conventional model. It was suggested that use of a TCS may contribute to a reduction in the AL rate.