Simplifying CT / MRI LI ‐ RADS v2018 Maintains Diagnostic Performance: An Individual Participant Data Meta‐Analysis
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.
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Authors: Eric Lam, Isabelle Boutron, R. G. Adamo, Christian B. van der Pol, Mostafa Alabousi, J. P. Salameh, Haresh Naringrekar, Mustafa Rifaat Bashir, Andreu F. Costa, H. Osman, D. Ansari, Rawan Awad, M. K. Al‐Ghita, Brooke Levis, A. Polikoff, A. Furlan, A. Tang, Andrea Siobhan Kierans, Amit G. Singal, A. Arvind, Ayman S. Alhasan, Brian C. Allen, B. Kim, C. S. Reiner, Chang Hee Lee, Christopher G D Clarke, C. Yang, Daniel R. Ludwig, D. Stocker, Federico Díaz Telli, Federico Piñero, Grzegorz Rosiak, Hanyu Jiang, Heejin Kwon, H. Wei, Hyo‐Jin Kang, Hyungjin Rhee, Ijin Joo, Jeong Ah Hwang, Ji Hye Min, J. S. Song, Jin Wang, Jin‐Young Choi, Joanna Podgórska, John Robert Eisenbrey, Keitaro Sofue, Koichiro Yasaka, Krzysztof Bartnik, Leonardo Centonze, A. Vanzulli, Li‐Da Chen, Marco Dioguardi Burgio, M. Ronot, Milena Cerny, Myeong‐Jin Kim, Nieun Seo, Shengxiang Rao, Roberto Cannella, Sang Hyun Choi, Tyler J. Fraum, W. Wang, Woo Kyoung Jeong, Xiang Jing, Yeun‐Yoon Kim, Matthew D. F. McInnes
Institutions: University of Pittsburgh Medical Center, Icahn School of Medicine at Mount Sinai, Dalhousie University, Fudan University, Zhongshan Hospital, Sun Yat-sen University, Sichuan University, West China Hospital of Sichuan University, University of North Carolina at Chapel Hill, Amsterdam University Medical Centers, Nottingham University Hospitals NHS Trust, University of Toronto, The Maria Sklodowska-Curie National Research Institute of Oncology, Yonsei University, University of Palermo, The University of Tokyo, Washington University in St. Louis, Korea University Medical Center, Samsung Medical Center, University of Ulsan, Asan Medical Center, Seoul National University Hospital, Thomas Jefferson University Hospital, University of Amsterdam, Université Paris Cité, Hôpital Beaujon, Catholic University of Korea, The First Affiliated Hospital, Sun Yat-sen University, Third Affiliated Hospital of Sun Yat-sen University, University Hospital Zurich, University of Ottawa, Ottawa Hospital, Taibah University, Sungkyunkwan University, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Azienda Socio Sanitaria Territoriale Grande Ospedale Metropolitano Niguarda, McGill University, McMaster University, The University of Texas Southwestern Medical Center, Duke Medical Center, Medical University of Warsaw, Seoul National University, Cooper University Hospital, Toronto Metropolitan University, University of Milan, Centre Hospitalier de l’Université de Montréal, Severance Hospital, King Faisal Specialist Hospital & Research Centre, The Catholic University of Korea Seoul St. Mary's Hospital, Hamilton Health Sciences, Ottawa Hospital Research Institute, Jeonbuk National University, Kobe University, Dong-A University, Dong-A University Hospital, Austral University, Tianjin Third Central Hospital, Centre de Recherche sur l'Inflammation, Jeonbuk National University Hospital, Hospital Universitario Austral, Amsterdam Public Health, Queen Elizabeth II Health Sciences Centre, Korea University Guro Hospital, Weill Cornell Medical Center, Juravinski Cancer Centre, Juravinski Hospital, William Osler Health System