BackgroundTo assess whether intraoperative use of contrast-enhanced ultrasound (CEUS)-CT/MR image fusion can accurately evaluate ablative margin (AM) and guide supplementary ablation to improve AM after hepatocellular carcinoma (HCC) ablation.MethodsNinety-eight patients with 126 HCCs designated to undergo thermal ablation treatment were enrolled in this prospective study. CEUS-CT/MR image fusion was performed intraoperatively to evaluate whether 5-mm AM was covered by the ablative area. If possible, supplementary ablation was applied at the site of inadequate AM. The CEUS image quality, the time used for CEUS-CT/MR image fusion and the success rate of image fusion were recorded. Local tumor progression (LTP) was observed during follow-up. Clinical factors including AM were examined to identify risk factors for LTP.ResultsThe success rate of image fusion was 96.2 % (126/131), and the duration required for image fusion was 4.9 ± 2.0 (3–13) min. The CEUS image quality was good in 36.1 % (53/147) and medium in 63.9 % (94/147) of the cases. By supplementary ablation, 21.8 % (12/55) of lesions with inadequate AMs became adequate AMs. During follow-up, there were 5 LTPs in lesions with inadequate AMs and 1 LTP in lesions with adequate AMs. Multivariate analysis showed that AM was the only independent risk factor for LTP (hazard ratio, 9.167; 95 % confidence interval, 1.070–78.571; p = 0.043).ConclusionCEUS-CT/MR image fusion is feasible for intraoperative use and can serve as an accurate method to evaluate AMs and guide supplementary ablation to lower inadequate AMs.

MWA is an effective treatment modality for BTNs. When considering the patient's comfort, cMWA would be a more preferable procedure with less complications.

Purpose: To evaluate whether local tumor progression (LTP) would be further reduced when contrastenhanced ultrasound (CEUS)-CT/MR fusion imaging was used as intraprocedural assessment method in hepatocellular carcinoma (HCC) thermal ablation compared with routine CEUS. Materials and methods: This prospective non-randomized study was conducted from December 2010 to July 2012. CEUS-CT/MR fusion imaging and routine CEUS were used for treatment response assessment in the ablation procedure of 146 HCCs and 122 HCCs, respectively. Supplementary ablations were performed immediately if necessary. The primary technique efficacy rate, LTP rate and overall survival (OS) rate were calculated. Results: For CEUS-CT/MR fusion imaging and routine CEUS, the technical success rate, technique efficacy rate and supplementary ablation rate were 86.3% (126/146) and 98.4% (120/122) (p ¼ .000), 99.2% (125/126) and 94.2% (113/120) (p ¼ .032), and 14.3% (18/126) and 4.2% (5/120) (p ¼ .006), respectively. The cumulative LTP rate and OS rate were not significantly different between fusion imaging group and routine CEUS group. However, for lesions that were larger than 3 cm or close to major vessels (41 lesions in fusion imaging group and 44 lesions in routine CEUS group, who received transcatheter arterial chemoembolization before ablation), the cumulative LTP rate was significantly lower in fusion imaging group than in routine CEUS group (p ¼ .032). Conclusion: Although intraprocedural CEUS-CT/MR fusion imaging has certain limitations in application, it might provide a potential more efficient method compared with routine CEUS in reducing LTP in HCC thermal ablation, especially for difficult ablation lesions.

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This study aimed to evaluate the diagnostic value of HyCoSy using sulfur hexafluoride microbubbles for fallopian tubal patency assessment in infertile females. Twenty-four studies, including 1358 females with 2661 detected fallopian tubes published from January 2003 to May 2019, were identified. The pooled sensitivity was 93% (95% CI: 90-95%), while the specificity was 90% (95% CI: 87-92%). The area under the receiver-operating characteristic curve was 0.96 (95% CI: 94-98%). The specificity of the four-dimensional HyCoSy subgroup was higher than the 2D/3D subgroup; an increased dose of contrast agent did not affect the specificity, with only a slightly reduced sensitivity.

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Objectives-Biliary perfusion is considered to contribute to biliary diseases, but routine imaging methods are insufficient to show it. This research investigated the ability of contrast-enhanced ultrasound (CEUS) for biliary perfusion in a biliary ischemia model.Methods-This research consisted of 2 parts. First, to determine whether CEUS enhancement of the tiny biliary wall represents biliary perfusion, a vascular tracer was used as a reference to evaluate the consistency with the enhancement of the biliary wall on CEUS and the staining by the vascular tracer under the conditions of occluded and recovered biliary perfusion. In the second part, the ability of CEUS for biliary ischemia was further evaluated with microvascular density measurement as a reference. The enhancement patterns were assigned CEUS scores, in which higher scores meant more decreased enhancement, and the diagnostic ability of CEUS was assessed by a receiver operating characteristic curve analysis.Results-The biliary wall was unstained by the vascular tracer and nonenhanced on CEUS when biliary perfusion was interrupted and was stained blue and enhanced after recovery. The biliary wall in the ischemia surgery group showed lower microvascular density measurements (P < .001), decreased enhancement levels (P < .001), and higher CEUS scores (P < .001). When a CEUS score of 3 or higher (obvious decrease of the biliary wall to hypoenhancement or nonenhancement in the arterial phase or rapid wash-out to nonenhancement in the portal venous phase) was applied, CEUS had sensitivity of 87.8%, specificity of 98.3%, accuracy of 93.8%, and an area under the receiver operating characteristic curve of 0.98.Conclusions-Contrast enhancement of the biliary wall on CEUS represents biliary perfusion and has reasonably good diagnostic performance for biliary ischemia in an experimental animal setting.

Recent developments of deep learning methods have demonstrated their feasibility in liver malignancy diagnosis using ultrasound (US) images. However, most of these methods require manual selection and annotation of US images by radiologists, which limit their practical application. On the other hand, US videos provide more comprehensive morphological information about liver masses and their relationships with surrounding structures than US images, potentially leading to a more accurate diagnosis. Here, we developed a fully automated artificial intelligence (AI) pipeline to imitate the workflow of radiologists for detecting liver masses and diagnosing liver malignancy. In this pipeline, we designed an automated mass-guided strategy that used segmentation information to direct diagnostic models to focus on liver masses, thus increasing diagnostic accuracy. The diagnostic models based on US videos utilized bi-directional convolutional long short-term memory modules with an attention-boosted module to learn and fuse spatiotemporal information from consecutive video frames. Using a large-scale dataset of 50 063 US images and video frames from 11 468 patients, we developed and tested the AI pipeline and investigated its applications. A dataset of annotated US images is available at https://doi.org/10.5281/zenodo.7272660.