An Integrated Clinical-Radiomics-Deep Learning Model Based on ¹⁸F-FDG PET/CT for Predicting EGFR Mutation Status in Lung Adenocarcinoma.

ABSTRACT:

Background

An integrated model combining clinical variables, radiomic features, and deep learning was developed to predict EGFR mutation status in patients with lung adenocarcinoma based on pretreatment ¹⁸F-FDG PET/CT imaging.

Methods

In this retrospective study, data from 218 patients-including PET/CT images, EGFR mutation status, and clinical characteristics-were analyzed. Three predictive models were constructed: a clinical model (C), a clinical-radiomics model (CR), and a clinical-radiomics-deep learning model (CRD).

Results

The CRD model integrated screened clinical features, as well as ConvNext-based deep learning scores and radiomic scores selected via LASSO regression. It exhibited significantly superior predictive performance to the C model (AUC = 

SUBMITTER: Wang Y 

PROVIDER: S-EPMC12631541 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature