Tissue attenuation imaging and tissue scatter imaging for quantitative ultrasound evaluation of hepatic steatosis.

We aimed to assess the feasibility of ultrasound-based tissue attenuation imaging (TAI) and tissue scatter distribution imaging (TSI) for quantification of liver steatosis in patients with nonalcoholic fatty liver disease (NAFLD). We prospectively enrolled 101 participants with suspected NAFLD. The TAI and TSI measurements of the liver were performed with a Samsung RS85 Prestige ultrasound system. Based on the magnetic resonance imaging proton density fat fraction (MRI-PDFF), patients were divided into ≤5%, 5-10%, and ≥10% of MRI-PDFF groups. We determined the correlation between TAI, TSI, and MRI-PDFF and used multiple linear regression analysis to identify any association with clinical variables. The diagnostic performance of TAI, TSI was determined based on the area under the receiver operating characteristic curve (AUC). The intraclass correlation coefficient (ICC) was calculated to assess interobserver reliability. Both TAI (rs = 0.78, P < .001) and TSI (rs = 0.68, P < .001) showed significant correlation with MRI-PDFF. TAI overperformed TSI in the detection of both ≥5% MRI-PDFF (AUC = 0.89 vs 0.87) and ≥10% (AUC = 0.93 vs 0.86). MRI-PDFF proved to be an independent predictor of TAI (ß = 1.03; P < .001), while both MRI-PDFF (ß = 50.9; P < .001) and liver stiffness (ß = -0.86; P < .001) were independent predictors of TSI. Interobserver analysis showed excellent reproducibility of TAI (ICC = 0.95) and moderate reproducibility of TSI (ICC = 0.73). TAI and TSI could be used successfully to diagnose and estimate the severity of hepatic steatosis in routine clinical practice.

Diagnosis of focal liver lesions with deep learning-based multi-channel analysis of hepatocyte-specific contrast-enhanced magnetic resonance imaging.

BACKGROUND: The nature of input data is an essential factor when training neural networks. Research concerning magnetic resonance imaging (MRI)-based diagnosis of liver tumors using deep learning has been rapidly advancing. Still, evidence to support the utilization of multi-dimensional and multi-parametric image data is lacking. Due to higher information content, three-dimensional input should presumably result in higher classification precision. Also, the differentiation between focal liver lesions (FLLs) can only be plausible with simultaneous analysis of multi-sequence MRI images. AIM: To compare diagnostic efficiency of two-dimensional (2D) and three-dimensional (3D)-densely connected convolutional neural networks (DenseNet) for FLLs on multi-sequence MRI. METHODS: We retrospectively collected T2-weighted, gadoxetate disodium-enhanced arterial phase, portal venous phase, and hepatobiliary phase MRI scans from patients with focal nodular hyperplasia (FNH), hepatocellular carcinomas (HCC) or liver metastases (MET). Our search identified 71 FNH, 69 HCC and 76 MET. After volume registration, the same three most representative axial slices from all sequences were combined into four-channel images to train the 2D-DenseNet264 network. Identical bounding boxes were selected on all scans and stacked into 4D volumes to train the 3D-DenseNet264 model. The test set consisted of 10-10-10 tumors. The performance of the models was compared using area under the receiver operating characteristic curve (AUROC), specificity, sensitivity, positive predictive values (PPV), negative predictive values (NPV), and f1 scores. RESULTS: The average AUC value of the 2D model (0.98) was slightly higher than that of the 3D model (0.94). Mean PPV, sensitivity, NPV, specificity and f1 scores (0.94, 0.93, 0.97, 0.97, and 0.93) of the 2D model were also superior to metrics of the 3D model (0.84, 0.83, 0.92, 0.92, and 0.83). The classification metrics of FNH were 0.91, 1.00, 1.00, 0.95, and 0.95 using the 2D and 0.90, 0.90, 0.95, 0.95, and 0.90 using the 3D models. The 2D and 3D networks' performance in the diagnosis of HCC were 1.00, 0.80, 0.91, 1.00, and 0.89 and 0.88, 0.70, 0.86, 0.95, and 0.78, respectively; while the evaluation of MET lesions resulted in 0.91, 1.00, 1.00, 0.95, and 0.95 and 0.75, 0.90, 0.94, 0.85, and 0.82 using the 2D and 3D networks, respectively. CONCLUSION: Both 2D and 3D-DenseNets can differentiate FNH, HCC and MET with good accuracy when trained on hepatocyte-specific contrast-enhanced multi-sequence MRI volumes.

Interobserver agreement and diagnostic accuracy of shearwave elastography for the staging of hepatitis C virus-associated liver fibrosis.

PURPOSE: Our study aimed to evaluate the technical success rate, interobserver reproducibility, and accuracy of shearwave elastography (SWE) in the staging of hepatitis C virus (HCV)-associated liver fibrosis. METHODS: A total of 10 healthy controls and 49 patients with chronic liver disease were enrolled prospectively. Two examiners performed point shearwave elastography (pSWE) and two-dimensional shearwave elastography (2D-SWE) measurements with an RS85A ultrasound scanner using the S-Shearwave application (Samsung Medison, Hongcheon, Korea). The performance of S-Shearwave in the staging (METAVIR F0-F4) of liver fibrosis was compared with prior transient elastography (TE) with receiver operating characteristic (ROC) curve analysis. RESULTS: The interobserver reproducibility was excellent with pSWE (ICC = 0.92, 95% CI: 0.86-0.95, P < .001). A very good agreement was found between pSWE and TE measurements (ICC =0.85, 95% CI: 0.78-0.89, P < .001). The ROC analysis determined the optimal cut-off values of pSWE for the staging of chronic hepatitis C-associated fibrosis (F2, 1.46 m/s; F3, 1.63 m/s; F4, 1.95 m/s). Both observers achieved excellent diagnostic accuracy (AUROC: 94% vs 97%) in the detection of significant (≥F2) liver fibrosis. CONCLUSION: The interobserver agreement is excellent with S-Shearwave pSWE, and observers can diagnose significant liver fibrosis with a comparable accuracy to TE.

Hereditary and acquired angioedema: problems and progress: proceedings of the third C1 esterase inhibitor deficiency workshop and beyond.

Hereditary angioedema (HAE), a rare but life-threatening condition, manifests as acute attacks of facial, laryngeal, genital, or peripheral swelling or abdominal pain secondary to intra-abdominal edema. Resulting from mutations affecting C1 esterase inhibitor (C1-INH), inhibitor of the first complement system component, attacks are not histamine-mediated and do not respond to antihistamines or corticosteroids. Low awareness and resemblance to other disorders often delay diagnosis; despite availability of C1-INH replacement in some countries, no approved, safe acute attack therapy exists in the United States. The biennial C1 Esterase Inhibitor Deficiency Workshops resulted from a European initiative for better knowledge and treatment of HAE and related diseases. This supplement contains work presented at the third workshop and expanded content toward a definitive picture of angioedema in the absence of allergy. Most notably, it includes cumulative genetic investigations; multinational laboratory diagnosis recommendations; current pathogenesis hypotheses; suggested prophylaxis and acute attack treatment, including home treatment; future treatment options; and analysis of patient subpopulations, including pediatric patients and patients whose angioedema worsened during pregnancy or hormone administration. Causes and management of acquired angioedema and a new type of angioedema with normal C1-INH are also discussed. Collaborative patient and physician efforts, crucial in rare diseases, are emphasized. This supplement seeks to raise awareness and aid diagnosis of HAE, optimize treatment for all patients, and provide a platform for further research in this rare, partially understood disorder.