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CT/MRI and CEUS LI-RADS Major Features Association with Hepatocellular Carcinoma: Individual Patient Data Meta-Analysis

Authors
 Christian B van der Pol  ;  Matthew D F McInnes  ;  Jean-Paul Salameh  ;  Brooke Levis  ;  Victoria Chernyak  ;  Claude B Sirlin  ;  Mustafa R Bashir  ;  Brian C Allen  ;  Lauren M B Burke  ;  Jin-Young Choi  ;  Sang Hyun Choi  ;  Alejandro Forner  ;  Tyler J Fraum  ;  Alice Giamperoli  ;  Hanyu Jiang  ;  Ijin Joo  ;  Zhen Kang  ;  Andrea S Kierans  ;  Hyo-Jin Kang  ;  Gaurav Khatri  ;  Jung Hoon Kim  ;  Myeong-Jin Kim  ;  So Yeon Kim  ;  Yeun-Yoon Kim  ;  Heejin Kwon  ;  Jeong Min Lee  ;  Sara C Lewis  ;  Katrina A McGinty  ;  Lorenzo Mulazzani  ;  Mi-Suk Park  ;  Fabio Piscaglia  ;  Joanna Podgórska  ;  Caecilia S Reiner  ;  Maxime Ronot  ;  Grzegorz Rosiak  ;  Bin Song  ;  Ji Soo Song  ;  An Tang  ;  Eleonora Terzi  ;  Jin Wang  ;  Wei Wang  ;  Stephanie R Wilson  ;  Takeshi Yokoo 
Citation
 RADIOLOGY, Vol.302(2) : 326-335, 2022-02 
Journal Title
RADIOLOGY
ISSN
 0033-8419 
Issue Date
2022-02
MeSH
Carcinoma, Hepatocellular / diagnostic imaging* ; Contrast Media ; Diagnosis, Differential ; Humans ; Liver Neoplasms / diagnostic imaging* ; Magnetic Resonance Imaging / methods ; Sensitivity and Specificity ; Tomography, X-Ray Computed / methods ; Ultrasonography / methods
Abstract
Background The Liver Imaging Reporting and Data System (LI-RADS) assigns a risk category for hepatocellular carcinoma (HCC) to imaging observations. Establishing the contributions of major features can inform the diagnostic algorithm. Purpose To perform a systematic review and individual patient data meta-analysis to establish the probability of HCC for each LI-RADS major feature using CT/MRI and contrast-enhanced US (CEUS) LI-RADS in patients at high risk for HCC. Materials and Methods Multiple databases (MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and Scopus) were searched for studies from January 2014 to September 2019 that evaluated the accuracy of CT, MRI, and CEUS for HCC detection using LI-RADS (CT/MRI LI-RADS, versions 2014, 2017, and 2018; CEUS LI-RADS, versions 2016 and 2017). Data were centralized. Clustering was addressed at the study and patient levels using mixed models. Adjusted odds ratios (ORs) with 95% CIs were determined for each major feature using multivariable stepwise logistic regression. Risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) (PROSPERO protocol: CRD42020164486). Results A total of 32 studies were included, with 1170 CT observations, 3341 MRI observations, and 853 CEUS observations. At multivariable analysis of CT/MRI LI-RADS, all major features were associated with HCC, except threshold growth (OR, 1.6; 95% CI: 0.7, 3.6; P = .07). Nonperipheral washout (OR, 13.2; 95% CI: 9.0, 19.2; P = .01) and nonrim arterial phase hyperenhancement (APHE) (OR, 10.3; 95% CI: 6.7, 15.6; P = .01) had stronger associations with HCC than enhancing capsule (OR, 2.4; 95% CI: 1.7, 3.5; P = .03). On CEUS images, APHE (OR, 7.3; 95% CI: 4.6, 11.5; P = .01), late and mild washout (OR, 4.1; 95% CI: 2.6, 6.6; P = .01), and size of at least 20 mm (OR, 1.6; 95% CI: 1.04, 2.5; P = .04) were associated with HCC. Twenty-five studies (78%) had high risk of bias due to reporting ambiguity or study design flaws. Conclusion Most Liver Imaging Reporting and Data System major features had different independent associations with hepatocellular carcinoma; for CT/MRI, arterial phase hyperenhancement and washout had the strongest associations, whereas threshold growth had no association.
Full Text
https://pubs.rsna.org/doi/10.1148/radiol.2021211244?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed
DOI
10.1148/radiol.2021211244
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Myeong Jin(김명진) ORCID logo https://orcid.org/0000-0001-7949-5402
Kim, Yeun-Yoon(김연윤) ORCID logo https://orcid.org/0000-0003-2018-5332
Park, Mi-Suk(박미숙) ORCID logo https://orcid.org/0000-0001-5817-2444
Choi, Jin Young(최진영) ORCID logo https://orcid.org/0000-0002-9025-6274
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/191248
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