Objective  To establish a radiomic model for predicting lymph node (LN) metastasis in patients with non-small cell lung cancer (NSCLC).

  Methods  The prediction model was developed using a training cohort comprising 100 patients with clinicopathologically confirmed NSCLC. Data were gathered from January 2014 to December 2015. Radiomic features of NSCLC were obtained from non-contrast and enhancement computed tomography (CT). Lasso-logistic regression models were established for data dimension reduction, feature selection, and radiomics signature building. Consistency coefficient (ICCs) was used to evaluate the consistency between observer interior and interobserver.The consistency index （C-index）is used to evalutate the prediction of lymph node metastasis by using the radiomics signature, shown with the area under the receiver operating characteristic curve (AUC).Multivariate logistic regression analyses were performed to develop the prediction model, considering radiomics signature and clinicopathologic risk factors. The radiomics model was validated in a validation cohort comprising 100 consecutive NSCLC patients from January 2016 to December 2017 in terms of its calibration and discrimination. AUC was used to evaluate the predictive effectiveness of the model, and Delong test was used to compare models. Hosmer-Lemeshow good of fit test was used to evaluate the calibration of prediction models.The results were represented by correction curves to compare the consistency between the predicted results of the model and the actual probability of LN metastasis.

  Results  The consistency between observer interior and interobserver was good, with ICC higher than 0.75.The radiomics signature, including 22 selected features, was associated with LN metastasis. AUC was 0.781 in training cohort and 0.776 in validation cohort. The individualized prediction model identified radiomics signature, neuron specific enolase (CEA), cytokeratin 19 fragment antigen 21-1 (CYFRA21-1), and carbohydrate antigen 125 (CA125) as independent predictors. The model showed good discrimination, with 0.836 AUC in the training cohort, and 0.821 AUC in the validation cohort. The model in both the training and validation cohorts had good calibration,which demonstrated high consistency with the actual LN metastasis.

  Conclusion  The radiomics model incorporating radiomics signature and clinical risk factors can be conveniently used to facilitate preoperative individualized prediction of LN metastasis in patients with NSCLC.