The value of CT radiomics in the prediction of lymph node metastasis in non-small cell lung cancer

ObjectiveTo explore the value of chest enhanced CT radiomics in the prediction of lymph node metastasis in patients with non-small cell lung cancer(NSCLC).

MethodsThe clinical and chest enhanced CT data of 143 NSCLC patients confirmed by pathologically were retrospectively analyzed.The patients were randomly divided into the training group(n=100) and verification group(n=43) according to the ratio of 7:3.The venous phase images were used to extract the radiomics features.The least absolute shrinkage and selection operator(LASSO) logistic regression was used for data dimension reduction and feature selection.Two predictive models were constructed using the radiomics features and clinical-imaging characteristics(the maximum meridian and burr sign).The AUCs of ROC was used to evaluate the predictive effectiveness of model.The ROC curve of model was tested by Delong test.The predictive efficacy was evaluated in validation group.

ResultsA total of 939 radiomics features were extracted, 6 optimal features were finally selected, and the prediction model was established.In the training group, the AUC of the radiomics model was 0.864(95%CI: 0.781~0.924), which was higher than that of the clinical model0.662 (95%CI: 0.561~0.754)(P < 0.01).In the validation group, the AUC of the radiomics model was 0.860(95%CI: 0.720~0.964), which was greater than that of the clinical model(0.664 (95%CI: 0.504~0.880)(P < 0.05).

ConclusionsBased on the image omics features extracted from the chest CT enhanced images and constructing the prediction model, the efficacy of the radiomics model was higher than that of the clinical model.The CT radiomics can be used as an auxiliary tool to predict lymph node metastasis in patients with non-small cell cancer, which has a good clinical application prospect.