An Integrated Clinical-Radiomics-Deep Learning Model Based on

backgroundAn 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

methodsIn 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).

resultsThe 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 = 0.599; DeLong test: Z = -3.522, p < 0.001, corrected p = 0.001) and the CR model (AUC = 0.739; DeLong test: Z = -2.197, p = 0.028, corrected p = 0.028), with an AUC of 0.821 for the CRD model. Calibration curves and decision curve analysis confirmed its robustness and potential clinical benefit. A nomogram based on the CRD model was established, enabling individualized risk prediction of EGFR mutation.

conclusionsThis study highlights the potential of integrating clinical, radiomic, and deep learning features as a noninvasive approach for accurately predicting EGFR mutation status in lung adenocarcinoma.