Objective Comparing the application value of CT radiomics and conventional imaging for predicting cervical lymph node metastasis in papillary thyroid cancer.Methods The clinical data of 86 patients diagnosed with papillary thyroid cancer in Chongqing Banan Hospital of Traditional Chinese Medicine from January 2019 to December 2023 were retrospectively analyzed,a total of 289 cervical lymph nodes were from 132 plain scan and 157 enhanced phase(venous phase)with pathological results.The enrolled lymph nodes were randomly divided into a plain scan group test set(n=40),training set(n=92)and a enhanced phase group test set(n=48),training set(n=109)in a ratio of 3∶7.The texture features of plain scan and enhanced phase(venous phase)were extracted by radiomics software,after dimensionality reduction and feature filtering by least absolute shrinkage and selection operator(LASSO)regression analysis,a linear support vector machine(SVM-Linear)classifier was trained to build this model for predicting lymph node metastasis.In the conventional imaging model.Two senior attending physicians interpret blindly the characteristics of randomly assigned lymph nodes,including size,shape,density,aspect ratio,enhancement,etc,the final result was decided by consensus.The predictive performance of CT radiomics and conventional imaging models was assessed by receiver operating characteristic(ROC)curve.Results After extracting 130 texture features of every image from the plain scan and the enhanced phase(venous phase),we finally screened out 15 features,including 7 plain scan and 8 enhanced phase features.The area under ROC curve(AUC)of the CT radiomics model in the plain scan group was 0.853,and the AUC of the conventional imaging model was 0.660.The AUC of the enhanced CT radiomic model was 0.940,and the AUC of the conventional imaging model was 0.765.Conclusion The predictive performance of CT radiomics is obviously better than that of conventional imaging,and it can accurately predict cervical lymph node metastasis in papillary thyroid cancer before clinical operation.
维普
万方数据
