Machine learning models predict lymph node metastasis in patients with stage T1-T2 esophageal squamous cell carcinoma.

ABSTRACT:

Background

For patients with stage T1-T2 esophageal squamous cell carcinoma (ESCC), accurately predicting lymph node metastasis (LNM) remains challenging. We aimed to investigate the performance of machine learning (ML) models for predicting LNM in patients with stage T1-T2 ESCC.

Methods

Patients with T1-T2 ESCC at three centers between January 2014 and December 2019 were included in this retrospective study and divided into training and external test sets. All patients underwent esophagectomy and were pathologically examined to determine the LNM status. Thirty-six ML models were developed using six modeling algorithms and six feature selection techniques. The optimal model was determined by the bootstrap method. An external test set was used to further assess the model's ge

SUBMITTER: Li DL 

PROVIDER: S-EPMC9496653 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature