Value of Simplified Pathological Classification of Thymus Epithelial Tumors Based on Plain CT Imaging Features
Objective To investigate the utility of CT plain scan based radiomics features in predicting who simplified pathologic classification of thymic epithelial neoplasms.Methods a total of 57 patients with thymic epithelial tumors(TETS)confirmed by pathological findings from January 2010 to March 2022 were retrospectively enrolled and divided into low-risk group(types A,AB,and B1)23 patients,high-risk group(types B2,B3,and C)34 patients by who simplified pathological classification of TETS,and randomly divided into training set and test set according to the ratio of 8:2.Each lesion was delineated by a region of interest(ROI)using itk-snap software after consultation by two radiologists.Radiomics features were extracted using Python v3.67 and further feature dimensionality reduction,filtering was performed by using Spearman's correlation coefficient,least absolute shrinkage and selection operator method(lasso).In the training set,three machine learning methods including support vector machine(SVM),multi-layer perceptual machine(MLP),and logistic regression(LR)were selected to construct the diagnostic prediction model.The receiver operating characteristic curve(ROC)was employed to evaluate its predictive efficacy,and an internal test set was applied to validate the above prediction model.Results a total of 1649 radiomics feature parameters were obtained,221 differential features were obtained by using Spearman correlation coefficients,and lasso was reduced to 12 omics features.The AUC values of the preoperative prediction model built by SVM,MLP and LR in the test set were 0.800,0.868,0.971,respectively,among which LR had better prediction.Condusbn in predicting the WHO simplified pathological classification of thymic epithelial tumors,the SVM,MLP,and LR models constructed based on CT plain imaging omics features have predictive potential,among which the LR model has better predictive effect.
NETL
NSTL
万方数据
维普
