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OBJECTIVES: To investigate microvascular changes in juvenile localised scleroderma (JLS) lesions using superb microvascular imaging (SMI) and assess SMI's utility in evaluating disease activity. METHODS: This prospective study enroled 16 children (7 males) with pathologically diagnosed JLS between January 2021 and June 2023. Lesions were assessed using Localised Scleroderma Cutaneous Assessment Tools, including the localised scleroderma skin activity index (LoSAI) and localised scleroderma skin damage index (LoSDI). Lesions with LoSAI scores > 0 were classified as active. The thickness and blood flow of the lesions and healthy skin layers of the contralateral site were evaluated using ultrasound. SMI was used to detect microvascular blood flow in the lesions and healthy skin, and the vascular index (VI) was calculated. The difference in VI between active lesions and healthy skin was correlated with LoSAI and total scores. RESULTS: Of 46 lesions, 23 were active and 23 inactive. The skin thickness of the lesion was 0.094 ± 0.024 cm, and that of the healthy site was 0.108 ± 0.026 cm (p < 0.001). The VI of the active lesions and healthy skin were 7.60 (3.60, 12.80)% and 1.10 (0.50, 2.10)%, respectively (p < 0.001). The VI of the inactive lesions and the healthy skin were 0.85 (0.00, 2.20)% and 1.60 (1.00, 3.10)%, respectively (p = 0.011). VI differences between active lesions and healthy skin positively correlated with the LoSAI clinical score (r = 0.625, p = 0.001) and total score (r = 0.842, p < 0.001). CONCLUSION: SMI can quantitatively detect microvascular blood flow changes in JLS skin, indicating lesion activity and severity. CLINICAL RELEVANCE STATEMENT: SMI is a convenient, non-invasive, technique for detecting active JLS lesions and can provide valuable information to guide treatment options. KEY POINTS: Current grading systems of juvenile localised scleroderma rely on subjective clinical information. Superb Microvascular Imaging identified that vascular indexes between active lesions and healthy skin positively correlated with clinical scores. Superb Microvascular Imaging effectively assesses microvascular blood flow, aiding juvenile localised scleroderma lesion activity evaluation.
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Background and aim: Diagnosing nonalcoholic steatohepatitis (NASH) is challenging. This study intended to explore the diagnostic value of multiple technical acoustic measurements in the diagnosis of NASH, and to establish a diagnostic model combining technical acoustic measurements with clinical parameters to improve the diagnostic efficacy of NASH. Methods: We consecutively enrolled 75 patients with clinically suspected nonalcoholic fatty liver disease (NAFLD) who underwent percutaneous liver biopsy in our hospital from June 2020 to December 2021. All cases underwent multiple advanced acoustic measurements for liver such as shear wave dispersion (SWD), shear wave speed (SWS), attenuation imaging (ATI), normalized local variance (NLV), and liver-kidney intensity ratio (Ratio) examination before liver biopsies. A nomogram prediction model combining the technical acoustic measurements and clinical parameters was established and the model is proposed to improve the diagnostic performance of NASH. Results: A total of 75 cases were included in this study. The classification of pathological grade for NASH was as follows: normal liver, (n = 15, 20%), nonalcoholic fatty liver (NAFL), (n = 44, 58.7%), and NASH, (n = 16, 21.3%). There were statistically significant differences in SWS (p = 0.002), acoustic coefficient (AC) (p = 0.018), NLV (p = 0.033), age (p = 0.013) and fasting blood glucose (Glu) (p = 0.049) between NASH and non-NASH. A nomogram model which includes SWS, AC, NLV, age and Glu was built to predict NASH, and the calibration curves showed good calibrations in both training and validation sets. The AUCs of the combined nomogram model for the training set and validation set were 0.8597 and 0.7794, respectively. Conclusion: There were statistically significant differences in SWS, AC, NLV, age and Glu between NASH and non-NASH. A nomogram model which includes SWS, AC, NLV, age and Glu was built to predict NASH. The predictive model has a higher diagnostic performance than a single factor model in the diagnosis of NASH and has good clinical application prospects.
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PURPOSE: The purpose of this study was to evaluate the diagnostic performance of novel ultrasound technology normalized local variance (NLV) and the standard deviation of NLV (NLV-SD) using different ROIs for hepatic steatosis in patients with metabolic-associated fatty liver disease (MAFLD) and to identify the factors that influence the NLV value and NLV-SD value, using pathology results as the gold standard. METHODS: We prospectively enrolled 34 consecutive patients with suspected MAFLD who underwent percutaneous liver biopsy for evaluation of hepatic steatosis from June 2020 to December 2020. All patients underwent ultrasound and NLV examinations. NLV values and NLV-SD values were measured using different ROIs just before the liver biopsy procedure. RESULTS: The distribution of hepatic steatosis grade on histopathology was 4/19/6/5 for none (< 5%)/ mild (5-33%)/ moderate (> 33-66%)/ and severe steatosis (> 66%), respectively. The NLV value with 50-mm-diameter ROI and NLV-SD value with 50-mm-diameter ROI showed a significant negative correlation with hepatic steatosis (spearman correlation coefficient: - 0.449, p = 0.008; - 0.471, p = 0.005). The AUROC of NLV (50 mm) for the detection of mild, moderate, and severe hepatic steatosis was 0.875, 0.735, and 0.583, respectively. The AUROC of NLV-SD (50 mm) for the detection of mild, moderate, and severe hepatic steatosis was 0.900, 0.745, and 0.603, respectively. NLV (50 mm) values and NLV-SD (50 mm) values between two readers showed excellent repeatability and the intraclass correlation coefficient (ICC) was 0.930 (p < 0.001) and 0.899 (p < 0.001). Hepatic steatosis was the only determinant factor for NLV value and NLV-SD value (p = 0.012, p = 0.038). CONCLUSION: The NLV (50 mm) and NLV-SD (50 mm) provided good diagnostic performance in detecting the varying degrees of hepatic steatosis with great reproducibility. This study showed that the degree of steatosis was the only significant factor affecting the NLV value and NLV-SD value.