Explainable PET-Based Habitat Modeling for Predicting Postoperative Recurrence Risk in Invasive Lung Adenocarcinoma

Postoperative recurrence remains a major clinical challenge in patients with resectable invasive adenocarcinoma of the lung (IAC). Conventional PET-based parameters and whole-tumor radiomics may insufficiently reflect the spatial heterogeneity relevant to recurrence risk. This study aimed to develop and validate an interpretable radiomics model based on [¹⁸F]FDG PET-derived habitat imaging for individualized recurrence risk prediction.

This retrospective study included 156 patients with pathologically confirmed IAC who underwent preoperative [¹⁸F]FDG PET/CT. Tumors were segmented and subdivided into intratumoral habitats using voxel-wise radiomic features and unsupervised clustering. A combined model was constructed by integrating clinical variables and radiomic features from the most predictive habitat. Model performance was assessed using receiver operating characteristic analysis, calibration curves, and decision curve analysis. Model interpretability was evaluated using Shapley Additive Explanations (SHAP). Prognostic value was assessed using Kaplan-Meier analysis based on disease-free survival (DFS).

The habitat-based combined model demonstrated the highest predictive performance, achieving an area under the curve of 0.823 in the test cohort, with good calibration and clinical utility. Stratification based on model output showed significant differences in DFS between high- and low-risk groups, with P < 0.0001 in the training cohort and P = 0.018 in the test cohort.

This PET-based habitat radiomics model provides a noninvasive and interpretable tool for preoperative prediction of postoperative recurrence in IAC. By accurately identifying patients at high risk of recurrence and reduced DFS, the model may support risk-adapted decision-making for postoperative management.