Development and validation of an interpretable medical deep foundation model for predicting occult lymph node metastasis in early-stage small cell lung cancer: a multicenter retrospective diagnostic study.

Background: Occult lymph node metastasis (OLM) is common in patients with clinical stage T1-2N0M0 small cell lung cancer (SCLC) and is strongly associated with inferior survival. Accurate preoperative identification of OLM remains challenging using conventional imaging, yet is critical for nodal staging, surgical planning, and individualized treatment decision-making. This study aimed to develop and externally validate an interpretable deep foundation model-based approach using preoperative contrast-enhanced computed tomography (CECT) to predict OLM in early-stage SCLC. Methods: In this multicenter retrospective diagnostic study, 416 surgically treated patients with postoperative pathologically confirmed SCLC and preoperative cT1-2N0M0 status were included. Patients from the largest center were randomly divided into training (n=201) and internal validation (n=86) cohorts, while patients from four additional centers constituted an external test cohort (n=129). Deep imaging features were extracted from preoperative CECT using a cancer-specific pretrained foundation model, and predictive models were constructed using machine-learning classifiers. Model performance was compared with handcrafted radiomics, conventional convolutional neural network (CNN)-based models, and a clinical model. Discrimination, calibration, and clinical utility were assessed using the area under the receiver operating characteristic curve (AUC), calibration analysis, and decision curve analysis. Results: The overall incidence of OLM was 33.9% (141/416). Among the evaluated imaging approaches, the foundation model-derived DFM50N achieved an external test AUC of 0.787 [95% confidence interval (CI): 0.703-0.871]. In unadjusted pairwise DeLong comparisons, its AUC was higher than those of RadN and the clinical model (P=0.02 and P=0.02, respectively), whereas no significant difference was observed compared with the combined model (AUC, 0.790; 95% CI: 0.705-0.874; P=0.90). The DFM50N model demonstrated relatively preserved external calibration and provided greater net benefit than strategies of additional staging for all or no patients across a clinically relevant range of threshold probabilities. Model interpretability analysis using SHapley Additive exPlanations (SHAP) revealed similar feature-ranking patterns associated with OLM risk across cohorts. Conclusions: An interpretable deep foundation model based on preoperative CECT achieved favorable and stable external performance for the noninvasive prediction of OLM in early-stage SCLC. The model showed favorable discrimination compared with handcrafted radiomics, conventional CNN-based approaches, and clinical assessment alone, and its integration with clinical predictors enabled individualized risk estimation. This approach may facilitate the identification of patients requiring more comprehensive mediastinal evaluation and support more informed multidisciplinary surgical planning and lymph node management.