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Background: We aimed to explore the value of combining real-time three-dimensional echocardiography (RT-3DE) and myocardial contrast echocardiography (MCE) in the left ventricle (LV) evaluating myocardial dysfunction in type 2 diabetes mellitus (T2DM) patients. Patients and Methods: A total of 58 T2DM patients and 32 healthy individuals were selected for this study. T2DM patients were further divided into T2DM without microvascular complications (n = 29) and T2DM with microvascular complications (n = 29) subgroups. All participants underwent RT-3DE and MCE. The standard deviation (SD) and the maximum time difference (Dif) of the time to the minimum systolic volume (Tmsv) of the left ventricle were measured by RT-3DE. MCE was performed to obtain the perfusion measurement of each segment of the ventricular wall, including acoustic intensity (A), flow velocity (ß), and A·ß. Results: There were significant differences in all Tmsv indices except for Tmsv6-Dif among the three groups (all P < 0.05). After heart rate correction, all Tmsv indices of the T2DM with microvascular complications group were prolonged compared with the control group (all P < 0.05). The parameters of A, ß, and A·ß for overall segments showed a gradually decreasing trend in three groups, while the differences between the three groups were statistically significant (all P < 0.01). For segmental evaluation of MCE, the value of A, ß, and A·ß in all segments showed a decreasing trend and significantly differed among the three groups (all P < 0.05). Conclusions: The RT-3DE and MCE can detect subclinical myocardial dysfunction and impaired myocardial microvascular perfusion. Left ventricular dyssynchrony occurred in T2DM patients with or without microvascular complications and was related to left ventricular dysfunction. Myocardial perfusion was reduced in T2DM patients, presenting as diffuse damage, which was aggravated by microvascular complications in other organs.
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BACKGROUND: The typical enhancement patterns of hepatocellular carcinoma (HCC) on contrast-enhanced ultrasound (CEUS) are hyper-enhanced in the arterial phase and washed out during the portal venous and late phases. However, atypical variations make a differential diagnosis both challenging and crucial. We aimed to investigate whether machine learning-based ultrasonic signatures derived from CEUS images could improve the diagnostic performance in differentiating focal nodular hyperplasia (FNH) and atypical hepatocellular carcinoma (aHCC). PATIENTS AND METHODS: A total of 226 focal liver lesions, including 107 aHCC and 119 FNH lesions, examined by CEUS were reviewed retrospectively. For machine learning-based ultrasomics, 3,132 features were extracted from the images of the baseline, arterial, and portal phases. An ultrasomics signature was generated by a machine learning model. The predictive model was constructed using the support vector machine method trained with the following groups: ultrasomics features, radiologist's score, and combination of ultrasomics features and radiologist's score. The diagnostic performance was explored using the area under the receiver operating characteristic curve (AUC). RESULTS: A total of 14 ultrasomics features were chosen to build an ultrasomics model, and they presented good performance in differentiating FNH and aHCC with an AUC of 0.86 (95% confidence interval [CI]: 0.80, 0.89), a sensitivity of 76.6% (95% CI: 67.5%, 84.3%), and a specificity of 80.5% (95% CI: 70.6%, 85.9%). The model trained with a combination of ultrasomics features and the radiologist's score achieved a significantly higher AUC (0.93, 95% CI: 0.89, 0.96) than that trained with the radiologist's score (AUC: 0.84, 95% CI: 0.79, 0.89, P < 0.001). For the sub-group of HCC with normal AFP value, the model trained with a combination of ultrasomics features, and the radiologist's score remain achieved the highest AUC of 0.92 (95% CI: 0.87, 0.96) compared to that with the ultrasomics features (AUC: 0.86, 95% CI: 0.74, 0.89, P < 0.001) and radiologist's score (AUC: 0.86, 95% CI: 0.79, 0.91, P < 0.001). CONCLUSIONS: Machine learning-based ultrasomics performs as well as the staff radiologist in predicting the differential diagnosis of FNH and aHCC. Incorporating an ultrasomics signature into the radiologist's score improves the diagnostic performance in differentiating FNH and aHCC.