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

作者：Yun Wang, Zhaoqing Chen, Jing Li, Yukun Cai, Changyu Sun, Jingjing Zhang, Marcus Hacker, Xiang Li, Heqing Yi · 发表于：Cancer Medicine · 年份：2025 · DOI：10.1002/cam4.71370 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Lung Cancer Diagnosis and Treatment、Cancer Immunotherapy and Biomarkers

BACKGROUND: F-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 = 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. CONCLUSIONS: This 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.