Machine learning-based prediction of duodenal stump leakage following laparoscopic gastrectomy for gastric cancer.

BACKGROUND

Duodenal stump leakage is one of the most critical complications following gastrectomy surgery, with a high mortality rate. The present study aimed to establish a predictive model based on machine learning for forecasting the occurrence of duodenal stump leakage in patients who underwent laparoscopic gastrectomy for gastric cancer.

MATERIALS AND METHODS

The present study included the data of 4,070 patients with gastric adenocarcinoma who received laparoscopic gastrectomy. Five algorithms, namely, k-nearest neighbors, logistic regression, random forest, support vector machine, and eXtreme Gradient Boosting, were used to establish models with the preoperative and intraoperative clinical features of patients. Performance assessment was carried out to determine the optimal model.

RESULTS

The present study involved 4,070 patients and incorporated 11 clinicopathologic features to construct machine learning models (males, 2,688, 66.0%; females, 1,382, 34.0%; age, 58 ± 11 years). Among the 5 algorithms, the support vector machine model exhibited the optimal performance, with an area under the curve of 0.866 (95% confidence interval, 0.803-0.928), sensitivity of 0.806, accuracy of 0.821, and specificity of 0.821. The analysis using the support vector machine model revealed that tumor location and clinic tumor stage significantly contributed to duodenal stump leakage.

CONCLUSION

The support vector machine model independently predicted duodenal stump leakage in patients with gastric cancer and exhibited favorable discrimination and accuracy. Thus, the construction of an efficient and intuitive online predictive tool demonstrated that the support vector machine model may exhibit potential in the prevention and adjunctive treatment of duodenal stump leakage. The model indicates that besides tumor location and stage, operation time, preoperative pyloric obstruction, and patient age also are important factors that have a significant impact on the occurrence of duodenal stump leakage after surgery.