Simplifying CT / MRI LI ‐ RADS v2018 Maintains Diagnostic Performance: An Individual Participant Data Meta‐Analysis

BACKGROUND: The Liver Imaging Reporting and Data System (LI-RADS) diagnostic algorithm assesses hepatocellular carcinoma (HCC) probability of liver observations in high-risk individuals, but complexity may limit its adoption. PURPOSE: To evaluate whether LI-RADS v2018 can be simplified without impairing diagnostic performance by removing threshold growth (TG) and reducing size cut-offs. STUDY TYPE: Retrospective meta-analysis. POPULATION: Eight thousand five hundred eighty-two patients (mean age: 59 ± 11 years; 6610 male) at risk for HCC with CT/MRI (986 CT, 6554 MRI, and 1042 CT/MRI). FIELD STRENGTH/SEQUENCE: Field strength: 1.5T and 3.0T; sequences varied by institution per LI-RADS technical requirements. ASSESSMENT: LI-RADS v2018 using major features only was compared with seven simplifications removing TG and/or applying single size cut-offs (≥ 10, ≥ 15, and ≥ 20 mm) in individual participant data (IPD) of 64 studies (29 institutions, 11 countries) from the LI-RADS Living Systematic Review and IPD Meta-analysis Project. Risk of bias was assessed with QUADAS-2. STATISTICAL TESTS: Summary receiver operating characteristic curves estimated area under the curve (AUC) across LR-3 to LR-5; LR-5 sensitivity, specificity, and positive predictive value (PPV) were estimated using a one-step bivariate mixed-effects model. Differences were assessed by cluster bootstrap. p < 0.05 indicated significance, uncorrected for multiple comparisons. RESULTS: Nine thousand four hundred six observations were included. Excluding TG with a 10-mm cut-off showed AUC similar to LI-RADS v2018 (0.80 vs. 0.81, p = 0.44) and significantly higher LR-5 sensitivity (73.8% vs. 71.8%) with similar specificity (72.5% vs. 73.7%, p = 0.13) and PPV (97.8% vs. 97.8%, p = 0.73). Removing TG with 15-mm and 20-mm cut-offs had significantly lower AUC than v2018 (-0.03 [95% CI: -0.041, -0.019]; -0.043 [-0.054, -0.032], respectively) and sensitivity (-13.2% [95% CI: -14.5%, -12.0%]; -30.1% [-31.7%, -28.5%], respectively). Of the 64 studies, 17 had low-risk of bias, and 56 had low applicability concerns. CONCLUSION: Simplifying LI-RADS v2018 by removing TG and using a 10-mm size cut-off preserved diagnostic table AUC while improving LR-5 sensitivity without loss of specificity or PPV. PROTOCOL REGISTRATION: Open Science Framework (https://osf.io/cjwgp). EVIDENCE LEVEL: 3. TECHNICAL EFFICIENCY: Stage 2.

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Journal
Journal of Magnetic Resonance Imaging
Published
2026-11-02
DOI
https://doi.org/10.1002/jmri.70592
Citations
1
Primary Topic
Hepatocellular Carcinoma Treatment and Prognosis
Type
article
Field-Weighted Citation Impact
3.49

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article

Simplifying CT / MRI LI ‐ RADS v2018 Maintains Diagnostic Performance: An Individual Participant Data Meta‐Analysis

Roberto Cannella, Daniel R. Ludwig, Shengxiang Rao, Grzegorz Rosiak et al.
1 citations
Journal of Magnetic Resonance Imaging
Hepatocellular Carcinoma Treatment and Prognosis
3.49
article

Simplifying CT / MRI LI ‐ RADS v2018 Maintains Diagnostic Performance: An Individual Participant Data Meta‐Analysis

Roberto Cannella, Daniel R. Ludwig, Shengxiang Rao, Grzegorz Rosiak, Haresh Naringrekar, Woo Kyoung Jeong, Federico Díaz Telli, Amit G. Singal, Leonardo Centonze, Hyungjin Rhee, Ijin Joo, Li‐Da Chen, Nieun Seo, Brooke Levis, Yeun‐Yoon Kim, Sang Hyun Choi, Rawan Awad, Ayman S. Alhasan, Brian C. Allen, Andreu F. Costa, Myeong‐Jin Kim, Federico Piñero, Andrea Siobhan Kierans, Heejin Kwon, Hanyu Jiang, Tyler J. Fraum, Ji Hye Min, Matthew D. F. McInnes, Krzysztof Bartnik, Joanna Podgórska, Christian B. van der Pol, Mustafa Rifaat Bashir, Mostafa Alabousi, Xiang Jing, Jeong Ah Hwang, Isabelle Boutron, John Robert Eisenbrey, C. S. Reiner, Marco Dioguardi Burgio, Keitaro Sofue, Milena Cerny, Hyo‐Jin Kang, Koichiro Yasaka, D. Ansari, Jin Wang, Chang Hee Lee, Jin‐Young Choi, Christopher G D Clarke, M. Ronot, Eric Lam, H. Osman, A. Polikoff, J. P. Salameh, A. Tang, H. Wei, M. K. Al‐Ghita, R. G. Adamo, B. Kim, D. Stocker, C. Yang, W. Wang, J. S. Song, A. Vanzulli, A. Furlan, A. Arvind
article en
1 citations

Abstract

BACKGROUND: The Liver Imaging Reporting and Data System (LI-RADS) diagnostic algorithm assesses hepatocellular carcinoma (HCC) probability of liver observations in high-risk individuals, but complexity may limit its adoption. PURPOSE: To evaluate whether LI-RADS v2018 can be simplified without impairing diagnostic performance by removing threshold growth (TG) and reducing size cut-offs. STUDY TYPE: Retrospective meta-analysis. POPULATION: Eight thousand five hundred eighty-two patients (mean age: 59 ± 11 years; 6610 male) at risk for HCC with CT/MRI (986 CT, 6554 MRI, and 1042 CT/MRI). FIELD STRENGTH/SEQUENCE: Field strength: 1.5T and 3.0T; sequences varied by institution per LI-RADS technical requirements. ASSESSMENT: LI-RADS v2018 using major features only was compared with seven simplifications removing TG and/or applying single size cut-offs (≥ 10, ≥ 15, and ≥ 20 mm) in individual participant data (IPD) of 64 studies (29 institutions, 11 countries) from the LI-RADS Living Systematic Review and IPD Meta-analysis Project. Risk of bias was assessed with QUADAS-2. STATISTICAL TESTS: Summary receiver operating characteristic curves estimated area under the curve (AUC) across LR-3 to LR-5; LR-5 sensitivity, specificity, and positive predictive value (PPV) were estimated using a one-step bivariate mixed-effects model. Differences were assessed by cluster bootstrap. p < 0.05 indicated significance, uncorrected for multiple comparisons. RESULTS: Nine thousand four hundred six observations were included. Excluding TG with a 10-mm cut-off showed AUC similar to LI-RADS v2018 (0.80 vs. 0.81, p = 0.44) and significantly higher LR-5 sensitivity (73.8% vs. 71.8%) with similar specificity (72.5% vs. 73.7%, p = 0.13) and PPV (97.8% vs. 97.8%, p = 0.73). Removing TG with 15-mm and 20-mm cut-offs had significantly lower AUC than v2018 (-0.03 [95% CI: -0.041, -0.019]; -0.043 [-0.054, -0.032], respectively) and sensitivity (-13.2% [95% CI: -14.5%, -12.0%]; -30.1% [-31.7%, -28.5%], respectively). Of the 64 studies, 17 had low-risk of bias, and 56 had low applicability concerns. CONCLUSION: Simplifying LI-RADS v2018 by removing TG and using a 10-mm size cut-off preserved diagnostic table AUC while improving LR-5 sensitivity without loss of specificity or PPV. PROTOCOL REGISTRATION: Open Science Framework (https://osf.io/cjwgp). EVIDENCE LEVEL: 3. TECHNICAL EFFICIENCY: Stage 2.

Journal of Magnetic Resonance Imaging
University of North Carolina at Chapel Hill (US), Dalhousie University (CA), Nottingham University Hospitals NHS Trust (GB), Seoul National University (KR), University of Ottawa (CA), Sun Yat-sen University (CN), University of Toronto (CA), University of Milan (IT), Yonsei University (KR), Austral University (AR), Washington University in St. Louis (US), Université Paris Cité (FR), Taibah University (SA), Sichuan University (CN), Fudan University (CN), Medical University of Warsaw (PL), Thomas Jefferson University Hospital (US), Hôpital Beaujon (FR), Juravinski Cancer Centre (CA), Severance Hospital (KR), Korea University Medical Center (KR), Queen Elizabeth II Health Sciences Centre (CA), Cooper University Hospital (US), Ottawa Hospital (CA), Dong-A University Hospital (KR), King Faisal Specialist Hospital & Research Centre (SA), Juravinski Hospital (CA), Asan Medical Center (KR), Samsung Medical Center (KR), Seoul National University Hospital (KR), Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (IT), University of Ulsan (KR), Tianjin Third Central Hospital (CN), West China Hospital of Sichuan University (CN), Azienda Socio Sanitaria Territoriale Grande Ospedale Metropolitano Niguarda (IT), Hospital Universitario Austral (AR), University Hospital of Zurich (CH), Amsterdam Public Health (NL), Duke Medical Center (US), Zhongshan Hospital (CN), The First Affiliated Hospital, Sun Yat-sen University (CN), William Osler Health System (CA), Centre de Recherche sur l'Inflammation (FR), Jeonbuk National University Hospital (KR), University of Pittsburgh Medical Center (US), Centre Hospitalier de l’Université de Montréal (CA), Third Affiliated Hospital of Sun Yat-sen University (CN), The Maria Sklodowska-Curie National Research Institute of Oncology (PL), Amsterdam University Medical Centers (NL), The Catholic University of Korea Seoul St. Mary's Hospital (KR), Ottawa Hospital Research Institute (CA), Korea University Guro Hospital (KR), Weill Cornell Medical Center (US), McGill University (CA), Dong-A University (KR), Toronto Metropolitan University (CA), Kobe University (JP), The University of Tokyo (JP), Hamilton Health Sciences (CA), Jeonbuk National University (KR), Sungkyunkwan University (KR), The University of Texas Southwestern Medical Center (US), Catholic University of Korea (KR), University of Amsterdam (NL), University of Palermo (IT), McMaster University (CA), Icahn School of Medicine at Mount Sinai (US)
Radiological Society of North America, Canadian Institutes of Health Research
Good health and well-being
Openalex Percentile: Top 5%
Hepatocellular Carcinoma Treatment and Prognosis
3.49
DOIOpen Access PDFOpenAlex
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