Development and validation of a radiomics-habitat model for preoperatively predicting poorly differentiated stage IA lung adenocarcinoma

Background: Stage IA poorly differentiated lung adenocarcinoma has stronger invasiveness and shorter survival time. Its treatment methods are related to the survival time, and preoperative prediction can assist clinical practice in formulating personalized treatment options. The aim of this study was to develop and validate a computed tomography (CT)-based radiomics-habitat fusion model for preoperative prediction of stage IA poorly differentiated lung adenocarcinoma.

Methods: A retrospective dual-center cohort of 507 patients with surgically confirmed stage IA lung adenocarcinoma was enrolled. The 419 patients from The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University (Center 1) were randomly assigned to a training cohort (n=294) and an internal test cohort (n=125), whereas 88 independent patients from Second Affiliated Hospital of Naval Medical University (Center 2) formed the external validation cohort. Tumor pathological differentiation graded by the 2021 World Health Organization (WHO) classification of thoracic tumors was set as the reference standard. Voxel-wise k-means clustering divided intratumoral regions into three distinct habitats (H1–3). Four machine learning algorithms [logistic regression, random forest, ExtraTrees, eXtreme Gradient Boosting (XGBoost)] were separately used to construct whole-tumor radiomics models, habitat subregional models and the final combined model. Model discrimination was comprehensively evaluated via area under the curve (AUC), sensitivity and specificity, etc.; calibration and clinical utility were assessed by calibration curves and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) analysis was applied to interpret feature contributions of the combined model.

Results: Poorly differentiated lesions accounted for 26.2% (133/507) of all participants. In the independent external validation cohort, the combined model demonstrated robust discriminatory performance with an AUC of 0.880 [95% confidence interval (CI): 0.794–0.966], significantly outperforming single models (P<0.05). At the optimized decision threshold, the combined model achieved a sensitivity of 0.84 and a specificity of 0.81. DCA confirmed that the combined model provided substantial net clinical benefit.

Conclusions: The developed and externally validated CT-based radiomics-habitat combined model serves as a promising non-invasive tool for preoperatively identifying poorly differentiated stage IA lung adenocarcinoma. Nevertheless, our external validation cohort only included 88 patients from a single independent center, which limits the generalizability of the model; further multi-center validation is warranted prior to clinical use.