The Predictive Value of 18 F-FDG PET/CT Radiomics in EGFR Gene Mutation of Lung Adenocarcinoma

Introduction: This study aimed to evaluate the predictive value of radiomic features derived from 18F-FluoroDeoxyGlucose (FDG) PET/CT for Epidermal Growth Factor Receptor (EGFR) gene mutations in patients with lung adenocarcinoma. Methods: A retrospective analysis was conducted on 93 patients diagnosed with solitary lung adenocarcinoma who underwent 18F-FDG PET/ CT imaging and EGFR mutation results. The patients were divided into training (46 cases) and testing (47 cases) cohorts. Radiomic features were extracted from the primary tumor sites' PET and CT images. Feature selection was performed using the Mann-Whitney U test and least absolute shrinkage and selection operator (LASSO) regression. A radiomics score (Rad-score) was constructed, and combined models incorporating clinical factors and metabolic parameters were developed. Predictive performance was evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC), accuracy, and decision curve analysis (DCA). Results: The radiomics model achieved AUCs of 0.865 (95% CI: 0.747–0.983) and 0.737 (95% CI: 0.572–0.901) in the training and testing sets, respectively, with corresponding accuracies of 80.9% and 78.3%. The clinical model alone demonstrated inferior performance, with AUCs of 0.637 and 0.645. The combined model showed slightly improved AUCs (0.885 and 0.714) but did not significantly outperform the radiomics-only model (P > 0.05). DCA indicated greater clinical utility for the radiomics model across a wide range of threshold probabilities. Discussion: PET/CT-based radiomics research has also achieved good efficacy in predicting EGFR gene mutations. Compared with morphological imaging techniques, such as X-ray, ultrasound, and CT, 18F-FDG PET/CT imaging has the significant advantage of providing functional and metabolic information of lesions. Both radiomics and composite models could predict EGFR mutation status in lung adenocarcinoma patients, but the radiomics model showed slightly better clinical predictive efficacy than the composite model. Conclusion: The radiomics model and the combined model integrating Rad-score with clinical factors demonstrated comparable abilities in effectively predicting EGFR mutation status in patients with lung adenocarcinoma. These models could offer a non-invasive approach for identifying EGFR mutations.