Applying machine learning techniques to predict the risk of distant metastasis from gastric cancer: a real world retrospective study.

ABSTRACT:

Background

Distant metastasis of gastric cancer can seriously affect the treatment strategy of gastric cancer patients, so it is essential to identify patients at high risk of distant metastasis of gastric cancer earlier.

Method

In this study, we retrospectively collected research data from 18,472 gastric cancer patients from the SEER database. We applied six machine learning algorithms to construct a model that can predict distant metastasis of gastric cancer. We constructed the machine learning model using 10-fold cross-validation. We evaluated the model using the area under the receiver operating characteristic curves (AUC), the area under the precision-recall curve (AUPRC), decision curve analysis, and calibration curves. In addition, we used Shapley's addition interpret

SUBMITTER: Qin X 

PROVIDER: S-EPMC11655338 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature