Citations

AbstractBackground and AimLiraglutide, a long‐acting GLP‐1 analog, is approved for the treatment of obesity with improvements in fasting blood glucose, hemoglobin A1c, and cardiovascular health. Our aim was to measure the impact of liraglutide dose for obesity on hepatic steatosis measured by ultrasound.MethodsA single‐center, randomized, double‐blind, placebo‐controlled pilot trial was undertaken in nondiabetic obese, otherwise healthy patients aged 18–65 years. Participants were randomly assigned to receive subcutaneous liraglutide (3.0 mg) or placebo over 16 weeks with dose escalation following US Food and Drug Administration guidelines. Both groups received standardized nutritional and behavioral counseling during the 16 weeks. Hepatic fat content was measured by ultrasound at baseline, 8 weeks, and 16 weeks as an attenuation coefficient (ACE). Effects of treatment were assessed using t‐test for the entire groups and for patient subgroup with baseline ACE >0.66 (indicating significant steatosis).ResultsAmong 30 patients (93% female) enrolled, 16 were randomized to placebo and 14 to liraglutide. Baseline body mass indices (BMIs) and average age were similar in the two groups. After 16 weeks, the liraglutide group had a significant improvement in steatosis ACE scores (−0.068 ± 0.02 vs −0.0077 ± 0.02 placebo, P = 0.05). Change in steatosis was positively correlated with change in BMI (R2 = 0.402, P = 0.0007). Within the liraglutide group, patients with baseline ACE >0.66 had improvement in ACE (−0.134 ± 0.03) compared to those without significant steatosis (−0.041 ± 0.02, P = 0.05).ConclusionsIn this pilot trial, liraglutide, 3.0 mg over 16 weeks, reduced hepatic steatosis; a reduction in hepatic steatosis is correlated with BMI reduction, and effects are particularly evident in those with a significant degree of steatosis by ultrasound imaging.

Objective The diagnosis of autoimmune pancreatitis (AIP) is challenging. Sonographic and cross-sectional imaging findings of AIP closely mimic pancreatic ductal adenocarcinoma (PDAC) and techniques for tissue sampling of AIP are suboptimal. These limitations often result in delayed or failed diagnosis, which negatively impact patient management and outcomes. This study aimed to create an endoscopic ultrasound (EUS)-based convolutional neural network (CNN) model trained to differentiate AIP from PDAC, chronic pancreatitis (CP) and normal pancreas (NP), with sufficient performance to analyse EUS video in real time. Design A database of still image and video data obtained from EUS examinations of cases of AIP, PDAC, CP and NP was used to develop a CNN. Occlusion heatmap analysis was used to identify sonographic features the CNN valued when differentiating AIP from PDAC. Results From 583 patients (146 AIP, 292 PDAC, 72 CP and 73 NP), a total of 1 174 461 unique EUS images were extracted. For video data, the CNN processed 955 EUS frames per second and was: 99% sensitive, 98% specific for distinguishing AIP from NP; 94% sensitive, 71% specific for distinguishing AIP from CP; 90% sensitive, 93% specific for distinguishing AIP from PDAC; and 90% sensitive, 85% specific for distinguishing AIP from all studied conditions (ie, PDAC, CP and NP). Conclusion The developed EUS-CNN model accurately differentiated AIP from PDAC and benign pancreatic conditions, thereby offering the capability of earlier and more accurate diagnosis. Use of this model offers the potential for more timely and appropriate patient care and improved outcome.

ObjectivesCrohn disease (CD) is a chronic inflammation in the digestive tract that affects millions of Americans. Bowel vascularity has important diagnostic information because inflammation is associated with blood flow changes. We recently developed an ultrasensitive ultrasound microvessel imaging (UMI) technique with high vessel sensitivity. This study aimed to evaluate the feasibility of UMI to assist CD detection and staging.MethodsUltrasound microvessel imaging was performed on 76 bowel wall segments from 48 symptomatic patients with CD. Clinically indicated computed tomographic/magnetic resonance enterography was used as the reference standard. The vessel‐length ratio (VLR, the number of vessel pixels in the bowel wall segment normalized to the segment length) was derived in both conventional color flow imaging (CFI) and UMI to quantitatively stage disease activity. Receiver operating characteristic curves were then analyzed between different disease groups.ResultsThe VLR‐CFI and VLR‐UMI detected similar correlations between vascularization and disease activity: severe inflammation had a higher VLR than normal/mildly inflamed bowels (P < .05). No significant difference was found between quiescent and mild CD due to the small sample size. The VLR‐CFI had more difficulties in distinguishing quiescent versus mild CD compared to the VLR‐UMI. After combining the VLR‐UMI with thickness, in the receiver operating characteristic curve analysis, the areas under the curves (AUCs) improved to AUC1 = 0.996 for active versus quiescent CD, AUC2 = 0.978 for quiescent versus mild CD, and AUC3 = 0.931 for mild versus severe CD, respectively, compared to those using thickness alone (AUC1 = 0.968; P = .04; AUC2 = 0.919; P = .16; AUC3 = 0.857; P = .01).ConclusionsUltrasound microvessel imaging offers a safe and cost‐effective tool for CD diagnosis and staging, which may potentially assist disease activity classification and therapy efficacy evaluation.

Abstract Rationale: Epstein–Barr virus (EBV)-related lymphoepithelioma-like cholangiocarcinoma (LELCC) is an extremely rare primary liver tumor with nonspecific clinical manifestations. The clinicopathological features of EBV-associated LELCC have been reported in a few cases. But reports of the tumor's imaging characteristics, particularly ultrasonographic findings, are very rare. Patient concerns: The first patient was a 64-year-old man with left upper quadrant pain and no nausea and dizziness for about 3 months. The second patient was a 40-year-old man, had an incidental finding of a hepatic tumor in a routine health checkup at a local hospital. Diagnoses: In the first patient, the abdominal ultrasound demonstrated a slightly heterogeneous hypoechoic nodule in segment 3 of the liver. The nodule was about 2.0 cm × 1.7 cm in size, with a clear margin and regular shape. Color Doppler flow imaging (CDFI) revealed no blood flow signals in this nodule. According to the clinical information and imaging features, it was difficult to determine the diagnosis of the nodule. In the second patient, gray-scale ultrasound revealed a slightly heterogeneous hypoechoic mass measuring 3.5 cm × 2.5 cm with well-defined margin and regular shape at the superior segment of the left hepatic lateral lobe. There was a blurrily hypoechoic halo around the mass. In contrast-enhanced ultrasound (CEUS), the mass was homogeneous hyperenhancement in the arterial phase. In the portal phase and late phase, the center enhancement of the mass washed out gradually, presenting hypoenhancement, Therefore, the tumor was diagnosed as malignancy. Intervention: Finally, a laparoscopic left hepatic lateral lobectomy was performed in the first patient. The second patient underwent a left hepatectomy with cholecystectomy. Outcomes: The first patient has been alive without recurrence or distant metastases for 11 months since the surgery. The second patient received routine follow-up after surgery. Until now, he has been tumor-free for 32 months. Lessons: We mainly focus on the ultrasound characteristics of EBV-associated LELCC, especially its enhancement patterns on CEUS, which may provide valuable information for diagnosis of the LELCC. When a liver tumor with typical CEUS patterns of malignancy is found in middle-aged adults with EBV positive, the possibility of EBV-related LELCC should be considered.

Strain-promoted alkyne–azide cycloaddition (SPAAC) click chemistry was applied for the rapid conjugation of nanoparticles, proteins, and siRNA-micelles to ultrasound microbubbles.

Ultrafast plane wave imaging enables acquisition and accumulation of a large number of Doppler ensembles in a short period of time, which substantially boosts ultrasound Doppler sensitivity by at least an order of magnitude. As a key component, spatiotemporal clutter filtering based on singular value decomposition (SVD) has recently demonstrated superior performance in tissue clutter suppression and microvessel signal extraction over conventional high-pass-filter-based clutter filtering techniques. This presentation introduces several novel SVD clutter filter techniques from our recent work, focusing on addressing the technical challenges (complex tissue motion, high computational cost, and noise) of SVD clutter filter during clinical translations of the ultrafast microvessel imaging technology for human imaging. We present a block-wise adaptive SVD filter method that can robustly threshold singular values on a local level, a real-time feasible SVD clutter filtering method based on partial SVD and randomized spatial downsampling, and a noise equalization and suppression method that can significantly improve microvessel imaging quality at a low cost. In vivo performance of these methods will be demonstrated in different organs.