Affiliation:
1. Department of Ultrasound The First Affiliated Hospital of Anhui Medical University Hefei Anhui Province China
2. Department of Ultrasound Medicine The Second Affiliated Hospital of Anhui Medical University Hefei China
3. Department of Ultrasound The Affiliated Yantai Yuhuangding Hospital of Qingdao University Yantai Shandong Province China
Abstract
AbstractPurposeTo explore the diagnostic value of intralesional and perilesional radiomics based on multimodal ultrasound (US) images in predicting the malignant ACR TIRADS 4 thyroid nodules (TNs).MethodsA total of 297 cases of TNs in patients who underwent preoperative thyroid grayscale US and shear wave elastography (STE) were enrolled (training cohort: n = 150, internal validation cohort: n = 77, external validation cohort: n = 70). Regions of interests (ROIs) were delineated on grayscale US images and STE images, and then an isotropic expansion of 1.0, 1.5, 2.0, 2.5, and 3.0 mm was applied. Predictive models were established using recursive feature elimination‐support vector machines (RFE‐SVM) based on radiomics features calculated by random forest.ResultsThe perilesional ROI1.5mm expansion achieved the highest area under curve (AUC) (AUC: 0.753 for grayscale US, 0.728 for STE; 95% confidence interval (CI): 0.664–0.743, 0.684–0.739, respectively). The joint model had the highest AUC values of 0.936 in the training dataset, 0.926 in internal dataset, and 0.893 in external dataset. The calibration curve showed good consistency and the decision curve indicated a greater clinical net benefit of the joint model.ConclusionJoint model containing perilesional radiomics (1.5 mm) had significant value in predicting the malignant ACR TIRADS 4 TNs.