CT-based radiomics and deep learning to predict EGFR mutation status in lung adenocarcinoma.

ABSTRACT:

Objectives

Epidermal growth factor receptor (EGFR) mutation status is an essential biomarker guiding targeted therapy selection in lung adenocarcinoma. This study aimed to develop and validate a non-invasive predictive model that integrates radiomics and deep learning using CT images for accurate assessment of EGFR mutation status.

Methods

A total of 220 patients with lung adenocarcinoma were retrospectively enrolled and randomly divided into training and testing cohorts at a 7:3 ratio. Radiomics features were extracted from CT images using PyRadiomics, and deep learning features were obtained from five pretrained architectures: ResNet34, ResNet152, DenseNet121, ShuffleNet, and Vision Transformer (ViT). Feature selection used the intraclass correlation coefficient, Spearman

SUBMITTER: Jiang X 

PROVIDER: S-EPMC12527853 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature