Objective:

To assess the utility of baseline 18F-FDG PET/CT radiomics by integrating tumor habitat analysis with intra- and peritumoral features to predict EGFR mutation status in lung adenocarcinoma.

Approach:

Key Findings:

• The combined model achieved the highest predictive performance for EGFR mutation (AUC = 0.862, 95% CI: 0.80–0.93).
• The habitat model followed with an AUC of 0.831 (95% CI: 0.76–0.90).
• Both models significantly outperformed all other models across datasets (all P < 0.05).
• The 6 mm expansion version of the peritumoral model 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.

Interpretation:

Baseline 18F-FDG PET/CT radiomics provides reliable prediction of EGFR mutation, with both habitat and combined models showing strong predictive performance.

Conclusion:

The study suggests that baseline 18F-FDG PET/CT radiomics can guide image-informed personalized treatment, with SHAP analysis enhancing interpretability for clinical implementation.

Attribution Notice

This content is an AI-generated, fully rewritten summary based on a published scholarly article. It does not reproduce the original text and is not a substitute for the original publication. Readers are encouraged to consult the source for full context, data, and methodology.