CT radiomics combined with clinical and CT features for predicting TNM stage of thymic epithelial tumor / 中国介入影像与治疗学

ABSTRACT

Objective To explore the value of CT radiomics combined with clinical data and CT features for predicting TNM stage of thymic epithelial tumor(TET).Methods Data of 216 single TET patients confirmed by surgical pathology were retrospectively analyzed.Totally 151 cases with TNM stage Ⅰ TET were divided into early group,while 27 with TNM stage Ⅲ and 38 with TNM stage Ⅳ TET were divided into late group(n=65).Univariate analysis was used to analyze clinical data and chest CT manifestations.Based on non-contrast-enhanced CT(NECT)and contrast-enhanced CT(CECT),the best radiomics features were extracted and screened to establish radiomics models(RMNECT,RMCECT)for predicting TNM stage of TET.RMNECT-clinic,RMCECT-clinic,RMNECT-clinic-CT and RMCECT-clinic-CT were constructed based on combination of clinical and CT features being significantly different between groups,respectively.The patients were divided into training set(n=151)and validation set(n=65)at the ratio of 7∶3.The above models were trained in the training set using repeated 5-fold cross validation method,and their efficacy were verified in the validation set.Results Significant differences of clinical symptoms and CT manifestations including fat infiltration around the lesion,mediastinal lymph node enlargement and pleural effusion were found between groups(all P＜0.05).Based on NECT and CECT,2 and 9 best radiomics features were selected to construct the corresponding models.In validation set,the area under the curve(AUC)of RMNECT-clinic-CT for predicting TNM stage of TET(0.864)was higher than that of RMNECT and RMNECT-clinic(AUC=0.634,0.721,Z=3.081,2.937,P=0.002,0.003),while AUC of RMCECT-clinic-CT(0.920)was also higher than that of RMCECT and RMCECT-clinic(AUC=0.689,0.751,Z=2.698,2.390,P=0.007,0.017).Conclusion CT radiomics combined with clinical data and CT features could effectively predict TNM stage of TET.