The predictive value of <sup>18</sup>F-FDG PET/CT habitat radiomics combined model in evaluating EGFR gene mutations in lung adenocarcinoma.
Authors
Affiliations (5)
Affiliations (5)
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, China.
- Department of Nuclear Medicine, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China.
- Department of Nuclear Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
- The Comprehensive Cancer Center of Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
- Clinical Cancer Institute of Nanjing University, Nanjing, China.
Abstract
This study assessed the utility of baseline <sup>18</sup>F-FDG PET/CT radiomics by integrating tumor habitat analysis with both intra- and peritumoral features to predict EGFR mutation status in lung adenocarcinoma. A total of 724 patients from two centers were allocated to training, validation, and test cohorts. Peritumoral regions were delineated with 2-8 mm radial expansions using LIFEx, while tumor habitat subregions were identified via k-means clustering. Multiple machine learning algorithms were employed to develop clinical-metabolic, intratumoral, peritumoral, habitat, and combined models. Model performance was assessed using AUC, calibration curves, DCA, and DeLong tests, and SHAP analysis was applied to interpret critical predictive features. In the test cohort, the combined model achieved the highest and most favorable predictive performance for EGFR mutation (AUC = 0.862, 95% CI: 0.80-0.93), followed by the habitat model (AUC = 0.831, 95% CI: 0.76-0.90). Both models significantly outperformed all other models across datasets (all <i>P</i> < 0.05). Among peritumoral models, the 6 mm expansion version demonstrated the highest AUC. SHAP analysis indicated that 16 of the 17 key features in the habitat model originated from Habitat 1 and 2 subregions, and approximately two-thirds of the top predictive features were CT-based. Baseline <sup>18</sup>F-FDG PET/CT radiomics provides reliable prediction of EGFR mutation. Both the habitat and combined models show comparable and strong predictive performance to guide image-informed personalized treatment, while SHAP analysis enhances interpretability for clinical implementation.