Pericardial Fat and Primary Tumor Radiomics for Predicting Occult N2 Disease and Survival in Clinical Stage I...

BACKGROUND. Occult N2 disease significantly affects clinical stage I non-small cell lung cancer (NSCLC) prognosis. Pericardial fat characteristics also have prognostic associations. OBJECTIVE. The purpose of this study was to develop and test a model incorporating pericardial fat and tumor radiomic features on CT for detecting occult N2 disease in clinical stage I NSCLC, explore the model's prognostic role, and investigate its biologic basis through radiogenomics analyses. METHODS. This retrospective study included patients who underwent clinical stage I NSCLC resection at three hospitals (center 1 [January 2016 to December 2022], stratified randomly by a 6:2:2 ratio into training, tuning, and internal test sets; centers 2 and 3 [January 2019 to December 2023], serving as external test sets). Pericardial fat and primary tumors were segmented on preoperative CT to extract radiomic features and generate tumor and fat radiomics scores (rad-scores), respectively. Multivariable analysis was performed to create a hybrid model for predicting occult N2 disease at surgery. Performance was evaluated in external test sets. Associations with recurrence-free survival (RFS) and overall survival (OS) were evaluated using log-rank tests in the internal test set; follow-up data were unavailable in the external test sets. Biologic mechanisms were explored through RNA and gene expression analysis in a separate set of patients with NSCLC obtained from a public radiogenomics database. RESULTS. From the three centers, 1662 patients (mean age, 58.6 years; 663 men, 999 women) were included. After multivariable analysis, the hybrid model included nodule density, fat rad-score, and tumor rad-score. The model had AUC, accuracy, sensitivity, and specificity for occult N2 disease of 0.921, 89.7%, 59.3%, and 93.1% and 0.913, 91.8%, 56.2%, and 95.5% in external test sets 1 and 2, respectively. In the internal test set, high-risk compared with low-risk patients showed worse RFS (p < .001) and OS (p < .001). In 122 patients in radiogenomics analysis, high-risk status was associated with activation of molecular pathways and increased activated dendritic cell and mast cell infiltration. CONCLUSION. A model incorporating tumor and pericardial fat radiomics showed good performance in predicting occult N2 disease as well as associations with survival and with RNA and gene expression. CLINICAL IMPACT. The model could help guide NSCLC management.