Objective:To explore the value of radiomics based on CT plain scan in preoperative prediction of central occult lymph node metastasis(OLNM)of papillary thyroid carcinoma(PTC).Methods:A retrospective analysis of clinical imaging data of PTC patients confirmed by pathology in Center 1 and Center 2 was made.Center 1 included 394 patients,who were randomly divided into a training set(276 cases)and an internal test set(118 cases)in a ratio of 7∶3,and Center 2 included 143 patients as an external test set.Radiomic features of lesions were extracted from CT plain scan images,the optimal features were obtained through dimensionality reduction,and five machine learning classifiers were established.The classifier with the highest average AUC value in the internal and the external test set was selected as the optimal radiomics model,and its results were converted into radiomics scores(Rad-scores).Clinical and conventional CT features with statistical difference in univariate analysis were included in multivariate logistic regression analysis to identify risk factors associated with OLNM and establish a clinical model.Subsequently,a combined prediction model was constructed based on clinical risk factors and Rad-scores,and a nomogram was drawn.ROC curve was used to evaluate the efficiency of the combined prediction model.Results:Ten optimal radiomics features were obtained from CT plain scan images.In the internal and external test set,extreme gradient boosting had the best predictive performance(average AUC of 0.782).The combined prediction model was further established with gender,maximum tumor diameter and Rad-scores,and AUCs of the combined prediction model in diagnosing PTC central OLNM in the training set,internal test set and external test set were 0.869,0.823 and 0.802,respectively.Conclusions:The radiomics features based on CT plain scan have good predictive values for central OLNM in PTC,and the combined prediction model established by clinical features and Rad-scores can better improve the diagnostic efficiency.
万方数据
维普
