Using nomogram, decision tree, and deep learning models to predict lymph node metastasis in patients with early gastric cancer: a multi-cohort study.

ABSTRACT: The accurate assessment of lymph node metastasis (LNM) in patients with early gastric cancer is critical to the selection of the most appropriate surgical treatment. This study aims to develop an optimal LNM prediction model using different methods, including nomogram, Decision Tree, Naive Bayes, and deep learning methods. In this study, we included two independent datasets: the gastrectomy set (n=3158) and the endoscopic submucosal dissection (ESD) set (n=323). The nomogram, Decision Tree, Naive Bayes, and fully convolutional neural networks (FCNN) models were established based on logistic regression analysis of the development set. The predictive power of the LNM prediction models was revealed by time-dependent receiver operating characteristic (ROC) curves and calibration plots. We then

SUBMITTER: Zhao L 

PROVIDER: S-EPMC9906085 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature