Objective:To construct a predictive model for lymph node metastasis(LNM)in non-small cell lung cancer(NSCLC)based on spectral CT radiomics,and to test its diagnostic efficacy.Methods:The imaging findings,tumor markers,and clinical data of 153 NSCLC patients who underwent spectral CT imaging examination,lung cancer resection surgery and lymph node dissection in our hospital from January 2019 to January 2023 were obtained.Patients were divided into training set(n=107)including 37 cases of LNM and 70 cases of non-LNM,and validation set(n=46)including 15 cases of LNM and 31 cases of non-LNM in a 7∶3 ratio.Data were used for training and verification of the models.The general clinical data and imaging features were compared between two sets.Regions of interest(ROI)of lung cancer lesions and target lymph nodes were delineated manually on CT plain and enhanced images.Artificial intelligence software was applied for extraction of texture pa-rameters for ROIs and the screening of texture parameters capable of identifying LNMs.LASSO regression was used to screen radiomics features and establish radiomics labels.A joint predictive model based on tumour tissue and target lymph node tex-ture CT parameters and radiomic features was constructed using the Multivariate Logistic regression algorithm.The area under the receiver operating characteristic(ROC)curve(AUC)was used to evaluate the diagnostic efficacy of spectral CT parameter model,radiomic signature model,and joint model for preoperative LNM.The DeLong test was used to compare the AUC dif-ferences of various models.Decision curve analysis(DCA)was used to evaluate the clinical benefits of the prediction model.A P<0.05 indicateed a statistically significant difference.Results:In both training set and validation set,the standardized iodine concentration(NIC)in the venous phase of LNM patients was lower than that of non LNM patients,and the lymph node short-axis diameter was higher than that of non LNM patients(P<0.05).A total of 207 radiomic features were extracted from images.After LASSO regression screening,5 radiomics features were finally included,including 2 grayscale size zone matrix features,2 grayscale run length matrix features,and 1 grayscale dependence matrix feature.Multivariate Logistic regression analysis de-noted that short-axis diameter of the lymph node,NIC of venous phase and Rad-score were independent factors in predicting LNM of NSCLC(P<0.05).ROC curve revealed that the areas under the curve(AUCs)for predicting the occurrence of LNM in the training set and verification set were 0.746 and 0.739 in spectral CT parameter model,the AUC were 0.747 and 0.726 in radiomic signature model,and were 0.847 and 0.813 in joint model.Delong test verified that the AUC of joint model was larger than that of spectral CT parameter model and radiomic signature model(P<0.05).Conclusion:Spectral CT radiomics has good predictive value for preoperative LNM in NSCLC.
维普
万方数据