Clinicopathological models for predicting lymph node metastasis in patients with early-stage lung adenocarcinoma: the application of machine learning algorithms.

ABSTRACT:

Background

Lymph node metastasis (LNM) status can be a critical decisive factor for clinical management of lung cancer. Accurately evaluating the risk of LNM during or after the surgery can be helpful for making clinical decisions. This study aims to incorporate clinicopathological characteristics to develop reliable machine learning (ML)-based models for predicting LNM in patients with early-stage lung adenocarcinoma.

Methods

A total of 709 lung adenocarcinoma patients with tumor size ≤2 cm were enrolled for analysis and modeling by multiple ML algorithms. The receiver operating characteristic (ROC) curve and decision curve were used for evaluating model's predictive performance and clinical usefulness. Feature selection based on potential models was performed to identify m

SUBMITTER: Chong Y 

PROVIDER: S-EPMC8339794 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature