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European Radiology
Published in: European Radiology 5/2020

01-05-2020 | Magnetic Resonance Imaging | Gastrointestinal

Does quantitative assessment of arterial phase hyperenhancement and washout improve LI-RADS v2018–based classification of liver lesions?

Authors: Daniel Stocker, Anton S. Becker, Borna K. Barth, Stephan Skawran, Malwina Kaniewska, Michael A. Fischer, Olivio Donati, Caecilia S. Reiner

Published in: European Radiology | Issue 5/2020

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Abstract

Objectives

To compare interreader agreement and diagnostic accuracy of LI-RADS v2018 categorization using quantitative versus qualitative MRI assessment of arterial phase hyperenhancement (APHE) and washout (WO) of focal liver lesions.

Methods

Sixty patients (19 female; mean age, 56 years) at risk for HCC with 71 liver lesions (28 HCCs, 43 benign) who underwent contrast-enhanced MRI were included in this retrospective study. Four blinded radiologists independently assigned a qualitative LI-RADS score per lesion. Two other radiologists placed ROIs within the lesion, adjacent liver parenchyma, and paraspinal musculature on pre- and post-contrast MR images. The percentage of arterial enhancement and the liver-to-lesion contrast ratio were calculated for quantification of APHE and WO. Using these quantitative parameters, a quantitative LI-RADS score was assigned. Interreader agreement and AUCs were calculated.

Results

Interreader agreement was similar for qualitative and quantitative LI-RADS (κ = 0.38 vs. 0.40–0.47) with a tendency towards improved agreement for quantitatively assessed APHE (κ = 0.65 vs. 0.81) and WO (κ = 0.53 vs. 0.78). Qualitative LI-RADS showed an AUC of 0.86, 0.94, 0.94, and 0.91 for readers 1, 2, 3, and 4, respectively. The quantitative LI-RADS score where APHE/WO/or both were replaced showed an AUC of 0.89/0.84/0.89, 0.95/0.92/0.92, 0.93/0.91/0.89, and 0.91/0.86/0.88 for readers 1, 2, 3, and 4, respectively. Sensitivity of LR-4/5 slightly increased, while specificity slightly decreased using quantitative APHE.

Conclusion

Qualitative and quantitative LI-RADS showed similar performance. Quantitatively assessed APHE showed the potential to increase interreader agreement and sensitivity of HCC diagnosis, whereas quantitatively assessed WO had the opposite effect and needs to be redefined.

Key Points

• Quantitative assessment of arterial phase hyperenhancement shows the potential to increase interreader agreement and sensitivity to diagnose hepatocellular carcinoma.
• Adding quantitative measurements of major LI-RADS features does not improve accuracy over qualitative assessment alone according to the LI-RADS v2018 algorithm.
Appendix
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Literature
1.
2.
Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A (2015) Global cancer statistics, 2012. CA Cancer J Clin 65:87–108CrossRef
3.
Kitao A, Zen Y, Matsui O, Gabata T, Nakanuma Y (2009) Hepatocarcinogenesis: multistep changes of drainage vessels at CT during arterial portography and hepatic arteriography--radiologic-pathologic correlation. Radiology 252:605–614CrossRef
4.
Matsui O, Kadoya M, Kameyama T et al (1991) Benign and malignant nodules in cirrhotic livers: distinction based on blood supply. Radiology 178:493–497CrossRef
5.
Sakamoto M, Hirohashi S, Shimosato Y (1991) Early stages of multistep hepatocarcinogenesis: adenomatous hyperplasia and early hepatocellular carcinoma. Hum Pathol 22:172–178CrossRef
6.
Takayama T, Makuuchi M, Hirohashi S et al (1990) Malignant transformation of adenomatous hyperplasia to hepatocellular carcinoma. Lancet 336:1150–1153CrossRef
7.
Lee YJ, Lee JM, Lee JS et al (2015) Hepatocellular carcinoma: diagnostic performance of multidetector CT and MR imaging-a systematic review and meta-analysis. Radiology 275:97–109CrossRef
8.
Hanna RF, Aguirre DA, Kased N, Emery SC, Peterson MR, Sirlin CB (2008) Cirrhosis-associated hepatocellular nodules: correlation of histopathologic and MR imaging features. Radiographics 28:747–769CrossRef
9.
10.
Barth BK, Donati OF, Fischer MA et al (2016) Reliability, validity, and reader acceptance of LI-RADS-an in-depth analysis. Acad Radiol 23:1145–1153CrossRef
11.
Davenport MS, Khalatbari S, Liu PS et al (2014) Repeatability of diagnostic features and scoring systems for hepatocellular carcinoma by using MR imaging. Radiology 272:132–142CrossRef
12.
Haimerl M, Wachtler M, Zeman F et al (2014) Quantitative evaluation of enhancement patterns in focal solid liver lesions with Gd-EOB-DTPA-enhanced MRI. PLoS One 9:e100315CrossRef
13.
Kim KW, Lee JM, Klotz E et al (2009) Quantitative CT color mapping of the arterial enhancement fraction of the liver to detect hepatocellular carcinoma. Radiology 250:425–434CrossRef
14.
Agarwal S, Grajo JR, Fuentes-Orrego JM et al (2016) Distinguishing hemangiomas from metastases on liver MRI performed with gadoxetate disodium: value of the extended washout sign. Eur J Radiol 85:635–640CrossRef
15.
Kloeckner R, Pinto Dos Santos D, Kreitner KF et al (2016) Quantitative assessment of washout in hepatocellular carcinoma using MRI. BMC Cancer 16:758CrossRef
16.
Liu YI, Shin LK, Jeffrey RB, Kamaya A (2013) Quantitatively defining washout in hepatocellular carcinoma. AJR Am J Roentgenol 200:84–89CrossRef
17.
Bosniak MA (1991) The small (less than or equal to 3.0 cm) renal parenchymal tumor: detection, diagnosis, and controversies. Radiology 179:307–317CrossRef
18.
Maki DD, Birnbaum BA, Chakraborty DP, Jacobs JE, Carvalho BM, Herman GT (1999) Renal cyst pseudoenhancement: beam-hardening effects on CT numbers. Radiology 213:468–472CrossRef
19.
Hotker AM, Mazaheri Y, Wibmer A et al (2017) Differentiation of clear cell renal cell carcinoma from other renal cortical tumors by use of a quantitative multiparametric MRI approach. AJR Am J Roentgenol 208:W85–W91CrossRef
20.
Kim SH, Kim CS, Kim MJ, Cho JY, Cho SH (2016) Differentiation of clear cell renal cell carcinoma from other subtypes and fat-poor angiomyolipoma by use of quantitative enhancement measurement during three-phase MDCT. AJR Am J Roentgenol 206:W21–W28CrossRef
21.
Young JR, Coy H, Kim HJ et al (2017) Performance of relative enhancement on multiphasic MRI for the differentiation of clear cell renal cell carcinoma (RCC) from papillary and chromophobe RCC subtypes and oncocytoma. AJR Am J Roentgenol 208:812–819CrossRef
22.
Becker AS, Barth BK, Marquez PH et al (2017) Increased interreader agreement in diagnosis of hepatocellular carcinoma using an adapted LI-RADS algorithm. Eur J Radiol 86:33–40CrossRef
23.
Hecht EM, Israel GM, Krinsky GA et al (2004) Renal masses: quantitative analysis of enhancement with signal intensity measurements versus qualitative analysis of enhancement with image subtraction for diagnosing malignancy at MR imaging. Radiology 232:373–378CrossRef
24.
Ho VB, Allen SF, Hood MN, Choyke PL (2002) Renal masses: quantitative assessment of enhancement with dynamic MR imaging. Radiology 224:695–700CrossRef
25.
Hallgren KA (2012) Computing inter-rater reliability for observational data: an overview and tutorial. Tutor Quant Methods Psychol 8:23–34CrossRef
26.
Viera AJ, Garrett JM (2005) Understanding interobserver agreement: the kappa statistic. Fam Med 37:360–363PubMed
27.
28.
Fowler KJ, Tang A, Santillan C et al (2018) Interreader reliability of LI-RADS version 2014 algorithm and imaging features for diagnosis of hepatocellular carcinoma: a large international multireader study. Radiology 286:173–185CrossRef
29.
An C, Park MS, Kim D et al (2013) Added value of subtraction imaging in detecting arterial enhancement in small (<3 cm) hepatic nodules on dynamic contrast-enhanced MRI in patients at high risk of hepatocellular carcinoma. Eur Radiol 23:924–930CrossRef
30.
Fischer MA, Kartalis N, Grigoriadis A et al (2015) Perfusion computed tomography for detection of hepatocellular carcinoma in patients with liver cirrhosis. Eur Radiol 25:3123–3132CrossRef
31.
Frericks BB, Loddenkemper C, Huppertz A et al (2009) Qualitative and quantitative evaluation of hepatocellular carcinoma and cirrhotic liver enhancement using Gd-EOB-DTPA. AJR Am J Roentgenol 193:1053–1060CrossRef
32.
Chou CT, Chen YL, Su WW, Wu HK, Chen RC (2010) Characterization of cirrhotic nodules with gadoxetic acid-enhanced magnetic resonance imaging: the efficacy of hepatocyte-phase imaging. J Magn Reson Imaging 32:895–902CrossRef
33.
Park Y, Kim YS, Rhim H, Lim HK, Choi D, Lee WJ (2009) Arterial enhancement of hepatocellular carcinoma before radiofrequency ablation as a predictor of postablation local tumor progression. AJR Am J Roentgenol 193:757–763CrossRef
34.
Chang WC, Chen RC, Chou CT et al (2014) Histological grade of hepatocellular carcinoma correlates with arterial enhancement on gadoxetic acid-enhanced and diffusion-weighted MR images. Abdom Imaging 39:1202–1212CrossRef
35.
Fowler KJ, Sirlin CB (2019) Is it time to expand the definition of washout appearance in LI-RADS? Radiology 291:658–659CrossRef
36.
Sofue K, Sirlin CB, Allen BC, Nelson RC, Berg CL, Bashir MR (2016) How reader perception of capsule affects interpretation of washout in hypervascular liver nodules in patients at risk for hepatocellular carcinoma. J Magn Reson Imaging 43:1337–1345CrossRef
Metadata
Title
Does quantitative assessment of arterial phase hyperenhancement and washout improve LI-RADS v2018–based classification of liver lesions?
Authors
Daniel Stocker
Anton S. Becker
Borna K. Barth
Stephan Skawran
Malwina Kaniewska
Michael A. Fischer
Olivio Donati
Caecilia S. Reiner
Publication date
01-05-2020
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 5/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-019-06596-9

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