Interreader Reliability of LI-RADS Version 2014 Algorithm and Imaging Features for Diagnosis of Hepatocellular Carcinoma: A Large International Multireader Study

被引:87
作者
Fowler, Kathryn J. [1 ]
Tang, An [2 ]
Santillan, Cynthia [3 ]
Bhargavan-Chatfield, Mythreyi [5 ]
Heiken, Jay [1 ]
Jha, Reena C. [6 ]
Weinreb, Jeffrey [7 ]
Hussain, Hero [8 ]
Mitchell, Donald G. [9 ]
Bashir, Mustafa R. [10 ]
Costa, Eduardo A. C. [11 ]
Cunha, Guilherme M. [12 ]
Coombs, Laura [5 ]
Wolfson, Tanya [4 ]
Gamst, Anthony C. [4 ]
Brancatelli, Giuseppe [13 ]
Yeh, Benjamin [14 ]
Sirlin, Claude B. [3 ]
机构
[1] Washington Univ, Sch Med, Mallinckrodt Inst Radiol, 510 S Kingshighway Blvd, St Louis, MO 63110 USA
[2] Ctr Hosp Univ Montreal, Dept Radiol, Montreal, PQ, Canada
[3] Univ Calif San Diego, Dept Radiol, Liver Imaging Grp, San Diego, CA 92103 USA
[4] Univ Calif San Diego, Computat & Appl Stat Lab, San Diego Supercomp Ctr, San Diego, CA 92103 USA
[5] Amer Coll Radiol, Reston, VA USA
[6] MedStar Georgetown Univ Hosp, Dept Radiol, Washington, DC USA
[7] Yale Med Sch, Dept Radiol, New Haven, CT USA
[8] Univ Michigan, Dept Radiol, Ann Arbor, MI 48109 USA
[9] Thomas Jefferson Univ, Dept Radiol, Philadelphia, PA 19107 USA
[10] Duke Univ, Med Ctr, Dept Radiol, Ctr Adv Magnet Resonance Dev, Durham, NC 27710 USA
[11] Cedrul CT & MRI, Joao Pessoa, Paraiba, Brazil
[12] Clin Diagnost Imagem CDPI DASA, Rio De Janeiro, Brazil
[13] Univ Palermo, Div Radiol Sci, Di Bi Med, Palermo, Italy
[14] Univ Calif San Francisco, Dept Radiol, San Francisco, CA USA
关键词
INTRACLASS CORRELATION-COEFFICIENT; DATA SYSTEM; CIRRHOTIC LIVER; MR; NODULES; CANCER; CT; CLASSIFICATION; ALLOCATION; WASHOUT;
D O I
10.1148/radiol.2017170376
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Purpose: To determine in a large multicenter multireader setting the interreader reliability of Liver Imaging Reporting and Data System (LI-RADS) version 2014 categories, the major imaging features seen with computed tomography (CT) and magnetic resonance (MR) imaging, and the potential effect of reader demographics on agreement with a preselected nonconsecutive image set. Materials and Methods: Institutional review board approval was obtained, and patient consent was waived for this retrospective study. Ten image sets, comprising 38-40 unique studies (equal number of CT and MR imaging studies, uniformly distributed LI-RADS categories), were randomly allocated to readers. Images were acquired in unenhanced and standard contrast material-enhanced phases, with observation diameter and growth data provided. Readers completed a demographic survey, assigned LI-RADS version 2014 categories, and assessed major features. Intraclass correlation coefficient (ICC) assessed with mixed-model regression analyses was the metric for interreader reliability of assigning categories and major features. Results: A total of 113 readers evaluated 380 image sets. ICC of final LI-RADS category assignment was 0.67 (95% confidence interval [CI]: 0.61, 0.71) for CT and 0.73 (95% CI: 0.68, 0.77) for MR imaging. ICC was 0.87 (95% CI: 0.84, 0.90) for arterial phase hyperenhancement, 0.85 (95% CI: 0.81, 0.88) for washout appearance, and 0.84 (95% CI: 0.80, 0.87) for capsule appearance. ICC was not significantly affected by liver expertise, LI-RADS familiarity, or years of postresidency practice (ICC range, 0.69-0.70; ICC difference, 0.003-0.01 [95% CI: -0.003 to -0.01, 0.004-0.02]. ICC was borderline higher for private practice readers than for academic readers (ICC difference, 0.009; 95% CI: 0.000, 0.021). Conclusion: ICC is good for final LI-RADS categorization and high for major feature characterization, with minimal reader demographic effect. Of note, our results using selected image sets from nonconsecutive examinations are not necessarily comparable with those of prior studies that used consecutive examination series. (C) RSNA, 2017.
引用
收藏
页码:173 / 185
页数:13
相关论文
共 32 条
[1]  
[Anonymous], 2015, OPTN UNOS POLICY 9 A
[2]  
[Anonymous], LIV IM REP DAT SYST
[3]   Reliability, Validity, and Reader Acceptance of LI-RADS - An In-depth Analysis [J].
Barth, Borna K. ;
Donati, Olivio F. ;
Fischer, Michael A. ;
Ulbrich, Erika J. ;
Karlo, Christoph A. ;
Becker, Anton ;
Seifert, Burkhard ;
Reiner, Caecilia S. .
ACADEMIC RADIOLOGY, 2016, 23 (09) :1145-1153
[4]   Concordance of hypervascular liver nodule characterization between the organ procurement and transplant network and liver imaging reporting and data system classifications [J].
Bashir, Mustafa R. ;
Huang, Rong ;
Mayes, Nicholas ;
Marin, Daniele ;
Berg, Carl L. ;
Nelson, Rendon C. ;
Jaffe, Tracy A. .
JOURNAL OF MAGNETIC RESONANCE IMAGING, 2015, 42 (02) :305-314
[5]   CME Update: Review Articles and Commentaries in JMRI [J].
Bashir, Mustafa R. ;
Korosec, Frank R. ;
Reeder, Scott B. .
JOURNAL OF MAGNETIC RESONANCE IMAGING, 2014, 40 (04) :778-778
[6]   Management of Hepatocellular Carcinoma: An Update [J].
Bruix, Jordi ;
Sherman, Morris .
HEPATOLOGY, 2011, 53 (03) :1020-1022
[7]   Natural history of liver imaging reporting and data system category 4 nodules in MRI [J].
Burke, Lauren M. B. ;
Sofue, Keitaro ;
Alagiyawanna, Madavi ;
Nilmini, Viragi ;
Muir, Andrew J. ;
Choudhury, Kingshuk R. ;
Semelka, Richard C. ;
Bashir, Mustafa R. .
ABDOMINAL RADIOLOGY, 2016, 41 (09) :1758-1766
[8]   Indeterminate Observations (Liver Imaging Reporting and Data System Category 3) on MRI in the Cirrhotic Liver: Fate and Clinical Implications [J].
Choi, Jin-Young ;
Cho, Hyun Cheol ;
Sun, Mark ;
Kim, Hyeon Chang ;
Sirlin, Claude B. .
AMERICAN JOURNAL OF ROENTGENOLOGY, 2013, 201 (05) :993-1001
[9]   Repeatability of Diagnostic Features and Scoring Systems for Hepatocellular Carcinoma by Using MR Imaging [J].
Davenport, Matthew S. ;
Khalatbari, Shokoufeh ;
Liu, Peter S. C. ;
Maturen, Katherine E. ;
Kaza, Ravi K. ;
Wasnik, Ashish P. ;
Al-Hawary, Mahmoud M. ;
Glazer, Daniel I. ;
Stein, Erica B. ;
Patel, Jeet ;
Somashekar, Deepak K. ;
Viglianti, Benjamin L. ;
Hussain, Hero K. .
RADIOLOGY, 2014, 272 (01) :132-142
[10]   Rate of observation and inter-observer agreement for LI-RADS major features at CT and MRI in 184 pathology proven hepatocellular carcinomas [J].
Ehman, Eric C. ;
Behr, Spencer C. ;
Umetsu, Sarah E. ;
Fidelman, Nicholas ;
Yeh, Ben M. ;
Ferrell, Linda D. ;
Hope, Thomas A. .
ABDOMINAL RADIOLOGY, 2016, 41 (05) :963-969