scholarly journals Evaluation of microvascular blood flow signals in focal liver lesions by ultrasound microvascular imaging

2016 ◽  
Vol 24 (21) ◽  
pp. 3304
Author(s):  
Shu Yang ◽  
Jie Li
2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Fan Yang ◽  
Jing Zhao ◽  
Chunwei Liu ◽  
Yiran Mao ◽  
Jie Mu ◽  
...  

Abstract Purpose To investigate the capacity of Superb Microvascular Imaging (SMI) to detect microvascular details and to explore the different SMI features in various focal liver lesions (FLLs) and the correlation between SMI and microvessel density (MVD). Method: Eighty-three liver lesions were enrolled in our study, including 35 hepatocellular carcinomas (HCCs) and 48 non-HCCs. All patients underwent color Doppler flow imaging (CDFI) and SMI examination and were categorized into subgroups according to Adler semiquantitative grading (grade 0–3) or the microvascular morphologic patterns (pattern a-f). The correlation between SMI blood flow signal percentage and MVD was assessed. Results Compared with CDFI, SMI detected more high-level blood flow signals (grade 2–3) and more hypervascular supply patterns (pattern e-f) in HCCs (p < 0.05). Furthermore, more hypervascular supply patterns and fewer hypovascular supply patterns were detected in HCC compared with non-HCC (p < 0.05). Based on Adler’s grading or microvascular morphologic patterns, the areas under the receiver operating characteristic curve were 0.696 and 0.760 for SMI, 0.583 and 0.563 for CDFI. The modality of “SMI-microvascular morphologic pattern” showed the best diagnostic performance. There was significant correlation between MVD and the SMI blood flow signal percentage (vascular index, VI) in malignant lesions (r = 0.675, p < 0.05). Conclusion SMI was superior to CDFI in detecting microvascular blood flow signals. More hypervascular supply patterns were depicted in HCC than in non-HCC, suggesting a promising diagnostic value for SMI in the differentiation between HCC and non-HCC. Meanwhile, we were the first to demonstrate that SMI blood flow signal percentage (VI) was correlated with MVD in malignant lesions.


2017 ◽  
Vol 23 (43) ◽  
pp. 7765-7775 ◽  
Author(s):  
Meng-Na He ◽  
Ke Lv ◽  
Yu-Xin Jiang ◽  
Tian-An Jiang

Medicine ◽  
2021 ◽  
Vol 100 (3) ◽  
pp. e24411
Author(s):  
Ping Sui ◽  
Xiaoyan Wang ◽  
Lipeng Sun ◽  
Hui Wang

Radiographics ◽  
2004 ◽  
Vol 24 (4) ◽  
pp. 921-935 ◽  
Author(s):  
Margot Brannigan ◽  
Peter N. Burns ◽  
Stephanie R. Wilson

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1715-P
Author(s):  
KATHERINE ROBERTS-THOMSON ◽  
RYAN D. RUSSELL ◽  
DONGHUA HU ◽  
TIMOTHY M. GREENAWAY ◽  
ANDREW C. BETIK ◽  
...  

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1244
Author(s):  
Sonja Schwarz ◽  
Dirk-André Clevert ◽  
Michael Ingrisch ◽  
Thomas Geyer ◽  
Vincent Schwarze ◽  
...  

Background: To evaluate the diagnostic accuracy of quantitative perfusion parameters in contrast-enhanced ultrasound to differentiate malignant from benign liver lesions. Methods: In this retrospective study 134 patients with a total of 139 focal liver lesions were included who underwent contrast enhanced ultrasound (CEUS) between 2008 and 2018. All examinations were performed by a single radiologist with more than 15 years of experience using a second-generation blood pool contrast agent. The standard of reference was histopathology (n = 60), MRI or CT (n = 75) or long-term CEUS follow up (n = 4). For post processing regions of interests were drawn both inside of target lesions and the liver background. Time–intensity curves were fitted to the CEUS DICOM dataset and the rise time (RT) of contrast enhancement until peak enhancement, and a late-phase ratio (LPR) of signal intensities within the lesion and the background tissue, were calculated and compared between malignant and benign liver lesion using Student’s t-test. Quantitative parameters were evaluated with respect to their diagnostic accuracy using receiver operator characteristic curves. Both features were then combined in a logistic regression model and the cumulated accuracy was assessed. Results: RT of benign lesions (14.8 ± 13.8 s, p = 0.005), and in a subgroup analysis, particular hemangiomas (23.4 ± 16.2 s, p < 0.001) differed significantly to malignant lesions (9.3 ± 3.8 s). The LPR was significantly different between benign (1.59 ± 1.59, p < 0.001) and malignant lesions (0.38 ± 0.23). Logistic regression analysis with RT and LPR combined showed a high diagnostic accuracy of quantitative CEUS parameters with areas under the curve of 0.923 (benign vs. malignant) and 0.929 (hemangioma vs. malignant. Conclusions: Quantified CEUS parameters are helpful to differentiate malignant from benign liver lesions, in particular in case of atypical hemangiomas.


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