The AFSUMB consensus statements and recommendations for the clinical practice of contrast-enhanced ultrasound using sonazoid

Lee, Jae Young; Minami, Yasunori; Choi, Byung Ihn; Lee, Won Jae; Chou, Yi‐Hong; Jeong, Woo Kyoung; Park, Mi‐Suk; Kudo, Nobuki; Lee, Min Woo; Kamata, Ken; Iijima, Hiroko; Kim, So Yeon; Numata, Kazushi; Sugimoto, Katsutoshi; Maruyama, Hitoshi; Sumino, Yasukiyo; Ogawa, Chikara; Kitano, Masayuki; Joo, Ijin; Arita, Junichi; Liang, Ja-Der; Lin, Hsi Ming; Nolsøe, Christian Pállson; Gilja, Odd Helge; Kudo, Masatoshi

doi:10.4103/jmu.jmu_124_19

Cited by 24 publications

(1 citation statement)

References 214 publications

(406 reference statements)

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“…Postvascular phase images using this property are effective for detecting occult hepatic lesions, differentiating between benign and malignant lesions, and evaluating therapeutic results. However, there are still several limitations in the diagnostic performance, such as with deeply located lesions and blind spots [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

AI-Based Radiological Imaging for HCC: Current Status and Future of Ultrasound

et al. 2021

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Hepatocellular carcinoma (HCC) is a common cancer worldwide. Recent international guidelines request an identification of the stage and patient background/condition for an appropriate decision for the management direction. Radiomics is a technology based on the quantitative extraction of image characteristics from radiological imaging modalities. Artificial intelligence (AI) algorithms are the principal axis of the radiomics procedure and may provide various results from large data sets beyond conventional techniques. This review article focused on the application of the radiomics-related diagnosis of HCC using radiological imaging (computed tomography, magnetic resonance imaging, and ultrasound (B-mode, contrast-enhanced ultrasound, and elastography)), and discussed the current role, limitation and future of ultrasound. Although the evidence has shown the positive effect of AI-based ultrasound in the prediction of tumor characteristics and malignant potential, posttreatment response and prognosis, there are still a number of issues in the practical management of patients with HCC. It is highly expected that the wide range of applications of AI for ultrasound will support the further improvement of the diagnostic ability of HCC and provide a great benefit to the patients.

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Section: Introductionmentioning

confidence: 99%

AI-Based Radiological Imaging for HCC: Current Status and Future of Ultrasound

et al. 2021

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Diagnostic Test Accuracy of Contrast‐Enhanced Ultrasound With Sonazoid for Assessment of Focal Liver Lesions: A Systematic Review and Meta‐Analysis

Tang,

Sasaki,

Hawley

et al. 2024

J of Clinical Ultrasound

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This meta‐analysis examined the diagnostic accuracy of Sonazoid‐enhanced ultrasonography (SZ‐CEUS) in discriminating malignant from benign focal liver lesions (FLLs) and HCC from non‐HCC FLLs. Finding relevant studies required a rigorous PubMed, EMBASE, and other database search. To distinguish malignant from benign FLLs, SZ‐CEUS had a pooled sensitivity of 94% (95% CI: 0.91–0.95) and specificity of 84% (95%: 0.78–0.89). HCC distinction had 83% sensitivity and 96% specificity (95% CI: 0.80–0.85 and 0.95–0.97). SZ‐CEUS accurately distinguishes malignant from benign FLLs and HCC from non‐HCC lesions, especially smaller HCC lesions.

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Contrast‐Enhanced Ultrasound WithSonoVueand Sonazoid for the Diagnosis of Colorectal Liver Metastasis After Chemotherapy

Sun

Yin

Xing

et al. 2022

J of Ultrasound Medicine

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Purpose To compare the diagnostic efficacy of SonoVue and Sonazoid contrast‐enhanced ultrasound (CEUS) in correctly detecting and characterizing colorectal liver metastasis (CRLM) after chemotherapy. Materials and methods Patients with CRLMs treated with chemotherapy and subsequently scheduled for hepatic resection were prospectively enrolled from April 2020 to January 2021. Lesions detected by SonoVue or Sonazoid CEUS were recorded as and characterized as metastases or non‐metastatic lesions respectively. Histopathology or intraoperative ultrasound with MRI were the reference standard. Results A total of 348 focal liver lesions in 42 patients were investigated, including 297 CRLMs and 51 non‐metastatic lesions. SonoVue showed significantly higher diagnostic accuracy (64.7% versus 54.0%; P < .001) and sensitivity (63.3% versus 50.5%; P < .001) in the diagnosis of CRLMs than Sonazoid, both methods presented with similar specificity (72.5% versus 74.5%; P = 1.0). Forty metastases appeared non‐hypoenhancing (hyperenhancing or isoenhancing) in the late phase and postvascular phase of Sonazoid CEUS and were mischaracterized as benign lesions. Conclusion SonoVue performed significantly better than Sonazoid in the diagnosis of CRLMs after chemotherapy.

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The AFSUMB consensus statements and recommendations for the clinical practice of contrast-enhanced ultrasound using sonazoid

Cited by 24 publications

References 214 publications

AI-Based Radiological Imaging for HCC: Current Status and Future of Ultrasound

AI-Based Radiological Imaging for HCC: Current Status and Future of Ultrasound

Diagnostic Test Accuracy of Contrast‐Enhanced Ultrasound With Sonazoid for Assessment of Focal Liver Lesions: A Systematic Review and Meta‐Analysis

Contrast‐Enhanced Ultrasound WithSonoVueand Sonazoid for the Diagnosis of Colorectal Liver Metastasis After Chemotherapy

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