A systematic review and meta-analysis of spectral CT to differentiate focal liver lesions

Bhandari, A., Koppen, J., Wastney, T., and Hacking, C. (2023) A systematic review and meta-analysis of spectral CT to differentiate focal liver lesions. Clinical Radiology, 78 (6). pp. 430-436.

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Abstract

Aim: To determine the feasibility of spectral computed tomography (CT) in the differentiation of focal liver lesions from hepatocellular carcinoma (HCC) using a network meta-analysis (NMA).

Materials and Methods: The review was completed in accordance with PRISMA guidelines. Searches of three medical databases were performed. A total of nine articles were found for the qualitative synthesis. The meta-analysis was performed on five studies for the normalised iodine concentration (NIC; which is the iodine concentration in the lesion divided by the iodine concentration in the aorta) and the lesion–normal parenchyma iodine ratio (LNR; which is the iodine concentration in the lesion divided by the iodine concentration in the non-tumour hepatic parenchyma) on portal venous and arterial phase images due to sufficient data.

Results: Spectral CT can be used to differentiate HCC from hepatic haemangioma (HH), focal nodular hyperplasia (FNH), regenerative nodules, neuroendocrine tumours (NETs), abscesses, and angiomyolipoma (AML). Hepatic metastases versus abscess and FNH versus HH could also be differentiated. The NMA demonstrated that HCC, NETs, and regenerative nodules could be differentiated due to lower quantitative iodine values. FNH, AML, and HH all had higher values.

Conclusion: Spectral CT shows promise in differentiating focal liver lesions. Studies with larger sample sizes are warranted. Future studies should be performed comparing benign lesions using quantitative markers.

Item ID: 78478
Item Type: Article (Research - C1)
ISSN: 1365-229X
Copyright Information: © 2023 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.
Date Deposited: 24 Oct 2023 00:54
FoR Codes: 40 ENGINEERING > 4003 Biomedical engineering > 400304 Biomedical imaging @ 100%
SEO Codes: 20 HEALTH > 2001 Clinical health > 200101 Diagnosis of human diseases and conditions @ 100%
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