Establishment of a machine learning model for predicting splenic hilar lymph node metastasis

Abstract

Upper gastrointestinal cancer (UGC) sometimes metastasizes to the splenic hilum lymph node (SHLN). However, surgical removal of SHLN is technically difficult, and the risk of postoperative complications is high. Although there are models that predict SHLN metastasis, they usually only provide point estimates of risk, and there is a lack of sufficient information. To address this issue, we aimed to develop a Bayesian logistic regression model called Bayes-SHLNM. The performance of the models was compared with that of the frequentist logistic regression (FLR) model as a benchmark, and the posterior probability distribution (PPD) was shown individually. The performance of Bayes-SHLNM was equivalent to that of the FLR model, and the PPD for each case was visualized as the uncertainty. These results indicate that the Bayes-SHLNM model has the potential to be used as a decision support system in clinical settings where uncertainty is high.