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BackgroundPrevious models for differentiating benign and malignant thyroid nodules(TN) have predominantly focused on the characteristics of the nodules themselves, without considering the specific features of the thyroid gland(TG) in patients with Hashimoto’s thyroiditis(HT). In this study, we analyzed the clinical and ultrasound radiomics(USR) features of TN in patients with HT and constructed a model for differentiating benign and malignant nodules specifically in this population.MethodsWe retrospectively collected clinical and ultrasound data from 227 patients with TN and concomitant HT(161 for training, 66 for testing). Two experienced sonographers delineated the TG and TN regions, and USR features were extracted using Python. Lasso regression and logistic analysis were employed to select relevant USR features and clinical data to construct the model for differentiating benign and malignant TN. The performance of the model was evaluated using area under the curve(AUC), calibration curves, and decision curve analysis(DCA).ResultsA total of 1,162 USR features were extracted from TN and the TG in the 227 patients with HT. Lasso regression identified 14 features, which were used to construct the TN score, TG score, and TN+TG score. Univariate analysis identified six clinical predictors: TI-RADS, echoic type, aspect ratio, boundary, calcification, and thyroid function. Multivariable analysis revealed that incorporating USR scores improved the performance of the model for differentiating benign and malignant TN in patients with HT. Specifically, the TN+TG score resulted in the highest increase in AUC(from 0.83 to 0.94) in the clinical prediction model. Calibration curves and DCA demonstrated higher accuracy and net benefit for the TN+TG+clinical model.ConclusionUSR features of both the TG and TN can be utilized for differentiating benign and malignant TN in patients with HT. These findings highlight the importance of considering the entire TG in the evaluation of TN in HT patients, providing valuable insights for clinical decision-making in this population.

IntroductionThe accurate assessment of the stages of hepatic fibrosis in children with biliary atresia (BA) before performing Kasai portoenterostomy (KPE) is of utmost importance. Some studies demonstrated that ultrasound elastography can be used to assess the stages of hepatic fibrosis by detecting liver stiffness. Therefore, the aim of this work was to explore the usefulness of sound touch elastography (STE) for preoperatively assessing liver cirrhosis in infants with BA.MethodsA total of 189 children from the Hunan Children’s Hospital with highly suspected BA were selected for this study, and their preoperative liver STE values and related clinical data were collected. The pathological results of the liver stages were considered as the gold standard. Spearman correlation was used to analyze the correlation between each parameter and the stage of hepatic fibrosis, and the receiver operator characteristic (ROC) curve was used to analyze the diagnostic performance for cirrhosis of each parameter.ResultsAmong the selected 189 patients with suspected BA, 159 were included in this study and were composed of 3 at hepatic fibrosis stage F1, 45 at stage F2, 41 at stage F3, and 70 at stage F4, while no patients at stage F0 were present. Spearman correlation analysis showed that the liver STE value had the highest correlation with the stage of hepatic fibrosis, with a correlation coefficient of 0.813 (P < 0.001). The liver STE value had the highest diagnostic performance for hepatic cirrhosis compared to other biomarkers of serum fibrosis; the area under the ROC curve was 0.899 when the best cut-off value was 14.57 kPa. The sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 78.6, 84.3, 79.7, 83.3, and 81.8%, respectively.ConclusionThe liver STE value has a high correlation with the stage of hepatic fibrosis in children with BA. STE has a good diagnostic performance in evaluating cirrhosis before KPE in children with BA.