A machine learning model for predicting the lymph node metastasis of early gastric cancer not meeting the endoscopic curability criteria.

ABSTRACT:

Background

We developed a machine learning (ML) model to predict the risk of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) who did not meet the existing Japanese endoscopic curability criteria and compared its performance with that of the most common clinical risk scoring system, the eCura system.

Methods

We used data from 4,042 consecutive patients with EGC from 21 institutions who underwent endoscopic submucosal dissection (ESD) and/or surgery between 2010 and 2021. All resected EGCs were histologically confirmed not to satisfy the current Japanese endoscopic curability criteria. Of all patients, 3,506 constituted the training cohort to develop the neural network-based ML model, and 536 constituted the validation cohort. The performance of our ML

SUBMITTER: Kato M 

PROVIDER: S-EPMC11335823 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature