Prognostic Value and Pathological Correlation of Peritumoral Radiomics in Surgically Resected Non-Small Cell Lung Cancer

Rationale and Objectives To determine the additional value of peritumoral radiomics in predicting overall survival (OS) in surgically resected non-small cell lung cancer (NSCLC) and its correlation with pathological findings. Methods A total of 526 patients with surgically resected NSCLC were included (191 training, 160 internal validation, and 175 external validation cohorts). CT images were used to segment the gross tumor volume (GTV) and peritumoral volume (PTV) within distances of 3, 6, 9 mm from the tumor boundary (PTV3, PTV6, and PTV9), and radiomic features were extracted. Four prognostic models for OS (GTV, GTV + PTV3, GTV + PTV6, and GTV + PTV9) were constructed using the training cohort. The prognostic ability and feature importance were evaluated using the validation cohorts. Pathological findings were compared between the two patient groups (n = 30 for each) having the top 30 and bottom 30 values of the most important peritumoral feature. Results The GTV + PTV3 models exhibited the highest predictive ability, which was higher than that of the GTV model in the internal validation cohort (C-index: 0.666 vs. 0.616, P = 0.027) and external validation cohort (C-index: 0.705 vs. 0.656, P = 0.048). The most important feature was GLDM_Dependence_Entropy, extracted from PTV3. High peritumoral GLDM_Dependence_Entropy was associated with a high proportion of invasive histological types, tumor spread through air spaces, and tumor-infiltrating lymphocytes (all P < 0.05). Conclusion The GTV and PTV3 combination demonstrated a higher prognostic ability, compared to GTV alone. Peritumoral radiomic features may be associated with various pathological prognostic factors. To determine the additional value of peritumoral radiomics in predicting overall survival (OS) in surgically resected non-small cell lung cancer (NSCLC) and its correlation with pathological findings. A total of 526 patients with surgically resected NSCLC were included (191 training, 160 internal validation, and 175 external validation cohorts). CT images were used to segment the gross tumor volume (GTV) and peritumoral volume (PTV) within distances of 3, 6, 9 mm from the tumor boundary (PTV3, PTV6, and PTV9), and radiomic features were extracted. Four prognostic models for OS (GTV, GTV + PTV3, GTV + PTV6, and GTV + PTV9) were constructed using the training cohort. The prognostic ability and feature importance were evaluated using the validation cohorts. Pathological findings were compared between the two patient groups (n = 30 for each) having the top 30 and bottom 30 values of the most important peritumoral feature. The GTV + PTV3 models exhibited the highest predictive ability, which was higher than that of the GTV model in the internal validation cohort (C-index: 0.666 vs. 0.616, P = 0.027) and external validation cohort (C-index: 0.705 vs. 0.656, P = 0.048). The most important feature was GLDM_Dependence_Entropy, extracted from PTV3. High peritumoral GLDM_Dependence_Entropy was associated with a high proportion of invasive histological types, tumor spread through air spaces, and tumor-infiltrating lymphocytes (all P < 0.05). The GTV and PTV3 combination demonstrated a higher prognostic ability, compared to GTV alone. Peritumoral radiomic features may be associated with various pathological prognostic factors.