CT-based decision tree model for predicting EGFR mutation status in synchronous multiple primary lung cancers.

ABSTRACT:

Background

The current study aimed to construct a computed tomography (CT)-based decision tree algorithm (DTA) model to predict the epidermal growth factor receptor (EGFR) mutation status in synchronous multiple primary lung cancers (SMPLCs).

Methods

The demographic and CT findings of 85 patients with molecular profiling for surgically resected SMPLCs were reviewed retrospectively. Least absolute shrinkage and selection operator (LASSO) regression was used to select the potential predictors of EGFR mutation, and a CT-DTA model was developed. Multivariate logistic regression analysis and receiver operating characteristic (ROC) curve analysis were performed to assess the performance of this CT-DTA model.

Results

The CT-DTA model was applied to predict the EGFR mutant th

SUBMITTER: Luo Y 

PROVIDER: S-EPMC10089846 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature