This study aimed to explore the ability of combination model of ultrasound radiomics score (Rad-score) and the thyroid imaging, reporting and data system by the American College of Radiology (ACR TI-RADS) in predicting benign and malignant thyroid nodules (TNs). Up to 286 radiomics features were extracted from ultrasound images of TNs. By using the lowest probability of classification error and average correlation coefficients (POE + ACC) and the least absolute shrinkage and selection operator (LASSO), we finally selected four features to establish Rad-score (Vertl-RLNonUni, Vertl-GLevNonU, WavEnLH-s4 and WavEnHL-s5). DeLong’s test and decision curve analysis (DCA) showed that the method of combining Rad-score and ACR TI-RADS had the best performance (the area under the receiver operating characteristic curve (AUC = 0.913 (95% confidence interval (CI), 0.881–0.939) and 0.899 (95%CI, 0.840–0.942) in the training group and verification group, respectively), followed by ACR TI-RADS (AUC = 0.898 (95%CI, 0.863–0.926) and 0.870 (95%CI, 0.806–0.919) in the training group and verification group, respectively), and followed by Rad-score (AUC = 0.750 (95%CI, 0.704–0.792) and 0.750 (95%CI, 0.672–0.817) in the training group and verification group, respectively). We concluded that the ability of ultrasound Rad-score to distinguish benign and malignant TNs was not as good as that of ACR TI-RADS, and the ability of the combination model of Rad-score and ACR TI-RADS to discriminate benign and malignant TNs was better than ACR TI-RADS or Rad-score alone. Ultrasound Rad-score might play a potential role in improving the differentiation of malignant TNs from benign TNs.

This study aimed to explore the feasibility of ultrasound radiomics analysis before invasive coronary angiography (ICA) for evaluating the severity of coronary artery disease (CAD) quantified by the SYNTAX score (SS). This study included 105 carotid plaques from 105 patients (64 low-SS patients, 41 intermediate-high-SS patients). The clinical characteristics and three-dimensional ultrasound (3D-US) features before ICA were assessed. Ultrasound images of carotid plaques were used for radiomics analysis. Least absolute shrinkage and selection operator (LASSO) regression, which generated several nonzero coefficients, was used to select features that could predict intermediate-high SS. Based on those coefficients, the radiomics score (Rad-score) was calculated. The selected clinical characteristics, 3D-US features, and Rad-score were finally integrated into a radiomics nomogram. Among the clinical characteristics and 3D-US features, high-density lipoprotein (HDL), apolipoprotein B (Apo B), and plaque volume were identified as predictors for distinguishing between low SS and intermediate-high SS. During the radiomics process, 8 optimal radiomics features most capable of identifying intermediate-high SS were selected from 851 candidate radiomics features. The differences in Rad-score between the training and the validation set were significant (p = 0.016 and 0.006). The radiomics nomogram integrating HDL, Apo B, plaque volume, and Rad-score showed excellent results in the training set (AUC, 0.741 (95% confidence interval (CI): 0.646–0.835)) and validation set (AUC, 0.939 (95% CI: 0.860–1.000)), with good calibration (mean absolute errors of 0.028 and 0.059 in training and validation sets, respectively). Decision curve analysis showed that the radiomics nomogram could identify patients who could obtain the most benefit. We concluded that the radiomics nomogram based on carotid plaque ultrasound has favorable value for the noninvasive prediction of intermediate-high SS. This radiomics nomogram has potential value for the risk stratification of CAD before ICA and provides clinicians with a noninvasive diagnostic tool.

Background: This study investigates the link between metabolic dysfunction-associated fatty liver disease (MAFLD) and coronary artery disease (CAD) using the ultrasonographic fatty liver indicator (US-FLI) to assess liver steatosis. Methods: A total of 204 patients were included, with hepatic steatosis evaluated through ultrasound characteristics, diagnosing fatty liver when US-FLI was ≥2. CAD severity was determined using the SYNTAX score (SS), categorizing 100 CAD patients into mild (SS ≤ 22) and moderate-severe (MS) (SS ≥ 23) groups. The association between US-FLI and SS in patients with MAFLD was evaluated through the multivariate logistic regression model. A receiver operating characteristic curve was applied to determine the accuracy, sensitivity, and specificity of US-FLI in predicting SS. Results: In the multivariate logistic regression analysis, US-FLI was an independent predictor of the CAD group (OR = 1.194, 95% CI: 1.008–1.414, p = 0.040) and the MS group (OR = 1.262, 95% CI: 1.025–1.553, p = 0.028). In the receiver operating characteristic curve analysis, a US-FLI value of 2 was found to be the optimal threshold point for diagnosing MS CAD patients (AUC = 0.620, 95% CI: 0.509–0.713, p = 0.039), with a sensitivity of 65.22% and a specificity of 55.56%. The diagnostic performance of MS CAD patients significantly improved when US-FLI was combined with type 2 diabetes mellitus (T2DM) (AUC = 0.732, 95% CI: 0.632–0.832, p < 0.001), with a sensitivity of 65.22% and specificity of 77.78%. Conclusions: US-FLI was independently and positively associated with CAD severity. US-FLI combined with T2DM had better diagnostic performance in patients with MS CAD.