Diagnostic Accuracy of Sonazoid-Enhanced Ultrasonography for Detection of Liver Metastasis
Anas Elgenidy, Khaled Saad, Reda Ibrahim, Aya Sherif, Taher Elmozugi, Moaz Y. Darwish, Mahmoud Abbas, Yousif A. Othman, Abdelrahman Elshimy, Alyaa M. Sheir, Dina H. Khattab, Abdallah A. Helal, Mario M. Tawadros, Osama Abuel-naga, Hazem I. Abdel-Rahman, Doaa Ali Gamal

TL;DR
This study evaluates how well Sonazoid-enhanced ultrasound can detect liver metastases, finding it to be a reliable diagnostic tool.
Contribution
The study provides a meta-analysis of Sonazoid-enhanced ultrasound's diagnostic accuracy for liver metastases, comparing it with intraoperative techniques.
Findings
Sonazoid-enhanced ultrasound has a pooled sensitivity of 0.88 and specificity of 0.92 for detecting liver metastases.
Contrast-enhanced intraoperative sonography shows higher sensitivity (0.93) and lower specificity (0.84) for liver metastases.
Both techniques demonstrate strong diagnostic odds ratios, indicating high accuracy in identifying liver metastases.
Abstract
Purpose: To evaluate the potential clinical role and reliability of Sonazoid-enhanced ultrasound (SEUS) as a diagnostic tool for liver metastatic lesions. Methods: An extensive literature search was conducted across five electronic databases, PubMed, Scopus, Embase, Cochrane Library, and Web of Science, from their inception up to January 2024 to identify all studies evaluating the use of Sonazoid-enhanced ultrasonography for detecting hepatic metastases. A meta-analysis was performed to assess diagnostic accuracy using the Meta-DiSc 2.0 software. Results: A total of 31 studies were included, 16 of which were eligible for meta-analysis and diagnostic test accuracy evaluation. A total of 13 studies in the meta-analysis evaluated the diagnostic accuracy of contrast-enhanced ultrasound (CEUS) for 1347 metastatic and 1565 non-metastatic liver lesions. The pooled sensitivity and specificity…
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Taxonomy
TopicsUltrasound and Hyperthermia Applications · Hepatocellular Carcinoma Treatment and Prognosis · Radiomics and Machine Learning in Medical Imaging
