Development and validation of a predictive model for lymph node metastases in peripheral non-small cell lung cancer with a tumor diameter ≤ 2.0 cm and a consolidation-to-tumor ratio > 0.5

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Background: Precisely predicting lymph node metastasis (LNM) status is critical for the treatment of early non-small5-cell lung cancer (NSCLC). In this study, we developed a LNM prediction tool for peripheral NSCLC with a tumor diameter ≤ 2.0 cm and consolidation-to-tumor ratio (CTR) > 0.5 to identify patients where segmentectomy could be applied. Methods: Clinical characteristics were retrospectively collected from 435 patients with NSCLC. Logistic regression analysis of the clinical characteristics of this development cohort was used to estimate independent LNM predictors. A prediction model was then developed and externally validated using a validation cohort at another institution. Results: Four independent predictors (tumor size, CTR, pleural indentation, and carcinoembryonic antigen (CEA) values) were identified and entered into the model. The model showed good calibration (Hosmer-Lemeshow (HL) P value = 0.680) with an area under the receiver operating characteristic curve (AUC) = 0.890 (95% confidence interval (CI): 0.808-0.972) in the validation cohort. Conclusions: We developed and validated a novel and effective model that predicted the probability of LNM for individual patients with peripheral NSCLC who had a tumor diameter ≤ 2.0 cm and CTR > 0.5. This model could help clinicians make individualized clinical decisions.