A machine learning-based model for predicting distant metastasis in patients with rectal cancer.

ABSTRACT:

Background

Distant metastasis from rectal cancer usually results in poorer survival and quality of life, so early identification of patients at high risk of distant metastasis from rectal cancer is essential.

Method

The study used eight machine-learning algorithms to construct a machine-learning model for the risk of distant metastasis from rectal cancer. We developed the models using 23867 patients with rectal cancer from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2017. Meanwhile, 1178 rectal cancer patients from Chinese hospitals were selected to validate the model performance and extrapolation. We tuned the hyperparameters by random search and tenfold cross-validation to construct the machine-learning models. We evaluated the models u

SUBMITTER: Qiu B 

PROVIDER: S-EPMC10465697 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature