ObjectiveThis study aimed to assess the accuracy of staging liver fibrosis in patients with non-alcoholic fatty liver disease (NAFLD) usingpoint shear wave elastography (pSWE) and transient elastography (TE).SettingRelevant records on NAFLD were retrieved from PubMed, Embase, Web of Science and the China National Knowledge Infrastructure databases up to 20 December 2017. A bivariate mixed-effects model was conducted to combine sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and area under the summary receiver operating characteristic curve (AUC) between pSWE and TE. A sensitivity analysis was implemented to explore the source of heterogeneity.ParticipantsPatients with NAFLD who had a liver stiffness measurement using pSWE and TE before liver biopsy were enrolled according to the following criteria: 2×2 contingency tables can be calculated via the reported number of cases; sensitivity and specificity were excluded according to the following criteria: history of other hepatic damage, such as chronic hepatitis C, concurrent active hepatitis B infection, autoimmune hepatitis, suspicious drug usage and alcohol abuse.ResultsNine pSWE studies comprising a total of 982 patients and 11 TE studies comprising a total of 1753 patients were included. For detection of significant fibrosis, advanced fibrosis and cirrhosis, the summary AUC was 0.86 (95% CI 0.83 to 0.89), 0.94 (95% CI 0.91 to 0.95) and 0.95 (95% CI 0.93 to 0.97) for pSWE, and the summary AUC was 0.85 (95% CI 0.82 to 0.88), 0.92 (95% CI 0.89 to 0.94) and 0.94 (95% CI 0.93 to 0.97) for TE, respectively. The proportion of failure measurement was over tenfold as common with TE using an M probe compared with pSWE.ConclusionpSWE and TE, providing precise non-invasive staging of liver fibrosis in NAFLD, are promising techniques, particularly for advanced fibrosis and cirrhosis.

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Small bowel phytobezoars are rare and almost always obstructive. There have been previously reported cases of phytobezoars in the literature, however there are few reports on radiological findings for small bowel bezoars. Barium studies characteristically show an intraluminal filling defect of variable size that is not fixed to the bowel wall with barium filling the interstices giving a mottled appearance. On CT scan, the presence of a round or ovoid intraluminal mass with a ‘mottled gas’ pattern is believed to be pathognomonic. Since features on CT scans are characteristics and physical findings are of little assistance in the diagnosis of bezoar, the diagnostic value of CT needs to be emphasised.

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Background: Identifying cervical lymph node metastasis (LNM) in primary thyroid cancer preoperatively using ultrasound is challenging. Therefore, a non-invasive method is needed to assess LNM accurately. Purpose: To address this need, we developed the Primary Thyroid Cancer Lymph Node Metastasis Assessment System (PTC-MAS), a transfer learning-based and B-mode ultrasound images-based automatic assessment system for assessing LNM in primary thyroid cancer. Methods: The system has two parts: YOLO Thyroid Nodule Recognition System (YOLOS) for obtaining regions of interest (ROIs) of nodules, and LMM assessment system for building the LNM assessment system using transfer learning and majority voting with extracted ROIs as input. We retained the relative size features of nodules to improve the system’s performance. Results: We evaluated three transfer learning-based neural networks (DenseNet, ResNet, and GoogLeNet) and majority voting, which had the area under the curves (AUCs) of 0.802, 0.837, 0.823, and 0.858, respectively. Method III preserved relative size features and achieved higher AUCs than Method II, which fixed nodule size. YOLOS achieved high precision and sensitivity on a test set, indicating its potential for ROIs extraction. Conclusions: Our proposed PTC-MAS system effectively assesses primary thyroid cancer LNM based on preserving nodule relative size features. It has potential for guiding treatment modalities and avoiding inaccurate ultrasound results due to tracheal interference.