Prediction of EGFR mutations in non-small cell lung cancer: a nomogram based on ¹⁸F-FDG PET and thin-section CT radiomics with machine learning.

ABSTRACT:

Background

This study aimed to develop and validate radiomics-based nomograms for the identification of EGFR mutations in non-small cell lung cancer (NSCLC).

Methods

A retrospective analysis was performed on 313 NSCLC patients, who were randomly divided into training (n = 250) and validation (n = 63) groups. Radiomic features were extracted from ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET) and thin-section computed tomography (CT) scans. After selecting optimal radiomic features, four machine learning algorithms, including logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost), were used to develop and validate radiomics models. A combined model, incorporating the Rad scor

SUBMITTER: Li J 

PROVIDER: S-EPMC11999825 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature