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Imaging prediction of nonalcoholic steatohepatitis using computed tomography texture analysis

  • Hepatobiliary-Pancreas
  • Published:
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Abstract

Objectives

To determine if texture analysis of non-contrast-enhanced CT (NECT) images is able to predict nonalcoholic steatohepatitis (NASH).

Methods

NECT images from 88 patients who underwent a liver biopsy for the diagnosis of suspected NASH were assessed and texture feature parameters were obtained without and with filtration. The patient population was divided into a predictive learning dataset and a validation dataset, and further divided into groups according to the prediction of liver fibrosis as assessed by hyaluronic acid levels. The reference standard was the histological result of a liver biopsy. A predictive model for NASH was developed using parameters derived from the learning dataset that demonstrated areas under the receiver operating characteristic curve (AUC) of >0.65. The resulting model was then applied to the validation dataset.

Results

In patients without suspected fibrosis, the texture parameter mean without filter and skewness with a 2-mm filter were selected for the NASH prediction model. The AUC of the predictive model for the validation dataset was 0.94 and the accuracy was 94%. In patients with suspicion of fibrosis, the mean without filtration and kurtosis with a 4-mm filter were selected for the NASH prediction model. The AUC for the validation dataset was 0.60 and the accuracy was 42%.

Conclusions

In patients without suspicion of fibrosis, NECT texture analysis effectively predicted NASH.

Key Points

• In patients without suspicion of fibrosis, NECT texture analysis effectively predicted NASH.

• The mean without filtration and skewness with a 2-mm filter were modest predictors of NASH in patients without suspicion of liver fibrosis.

• Hepatic fibrosis masks the characteristic texture features of NASH.

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Abbreviations

ALB:

Albumin

ALT:

Alanine aminotransferase

AST:

Aspartate aminotransferase

AUC:

Area under the ROC curve

INR:

International normalized ratio

NAFLD:

Nonalcoholic fatty liver disease

NAS:

NAFLD activity score

NASH:

Nonalcoholic steatohepatitis

NECT:

Non-contrast-enhanced computed tomography

PT:

Prothrombin time

ROC:

Receiver operating characteristic

ROI:

Region of interest

SD:

Standard deviation

SSF:

Spatial scaling factors

T-Bil:

Total bilirubin

References

  1. Lazo M, Hernaez R, Eberhardt MS et al (2013) Prevalence of nonalcoholic fatty liver disease in the United States: The Third National Health and Nutrition Examination Survey, 1988-1994. Am J Epidemiol 178:38–45

    Article  PubMed  PubMed Central  Google Scholar 

  2. Goh GB-B, McCullough AJ (2016) Natural history of nonalcoholic fatty liver disease. Dig Dis Sci 61:1226–1233

    Article  PubMed  CAS  Google Scholar 

  3. Chalasani N, Younossi Z, Lavine JE et al (2012) The diagnosis and management of non-alcoholic fatty liver disease: Practice Guideline by the American Association for the Study of Liver Diseases, American College of Gastroenterology, and the American Gastroenterological Association. Hepatology 55:2005–2023

    Article  PubMed  Google Scholar 

  4. Salman AAA, Aboelfadl SAE, Heagzy MAE (2016) New era for usage of serum liver enzymes as a promising horizon for the prediction of non-alcoholic fatty liver disease. Open Access Maced J Med Sci 4:348–352

    Article  PubMed  PubMed Central  Google Scholar 

  5. Pulzi FB, Cisternas R, Melo MR et al (2011) New clinical score to diagnose nonalcoholic steatohepatitis in obese patients. Diabetol Metab Syndr 3:3

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Kwok R, Tse Y-K, Wong GL-H et al (2014) Systematic review with meta-analysis: non-invasive assessment of non-alcoholic fatty liver disease – the role of transient elastography and plasma cytokeratin-18 fragments. Aliment Pharmacol Ther 39:254–269

    Article  PubMed  CAS  Google Scholar 

  7. Verma S, Jensen D, Hart J, Mohanty SR (2013) Predictive value of ALT levels for non-alcoholic steatohepatitis (NASH) and advanced fibrosis in non-alcoholic fatty liver disease (NAFLD). Liver Int 33:1398–1405

    Article  PubMed  CAS  Google Scholar 

  8. Cusi K, Chang Z, Harrison S et al (2014) Limited value of plasma cytokeratin-18 as a biomarker for NASH and fibrosis in patients with non-alcoholic fatty liver disease. J Hepatol 60:167–174

    Article  PubMed  CAS  Google Scholar 

  9. Daginawala N, Li B, Buch K et al (2016) Using texture analyses of contrast enhanced CT to assess hepatic fibrosis. Eur J Radiol 85:511–517

    Article  PubMed  Google Scholar 

  10. Zhang X, Gao X, Liu BJ et al (2015) Effective staging of fibrosis by the selected texture features of liver: Which one is better, CT or MR imaging? Comput Med Imaging Graph 46:227–236

    Article  PubMed  Google Scholar 

  11. Kayaaltı Ö, Aksebzeci BH, Karahan İÖ et al (2014) Liver fibrosis staging using CT image texture analysis and soft computing. Appl Soft Comput 25:399–413

    Article  Google Scholar 

  12. Davnall F, Yip CSP, Ljungqvist G et al (2012) Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging 3:573–589

    Article  PubMed  PubMed Central  Google Scholar 

  13. Raman SP, Schroeder JL, Huang P et al (2015) Preliminary data using computed tomography texture analysis for the classification of hypervascular liver lesions. J Comput Assist Tomogr 39:383–395

    PubMed  Google Scholar 

  14. Ganeshan B, Miles KA (2013) Quantifying tumour heterogeneity with CT. Cancer Imaging 13:140–149

    Article  PubMed  PubMed Central  Google Scholar 

  15. Kassner A, Thornhill RE (2010) Texture analysis: a review of neurologic MR imaging applications. AJNR Am J Neuroradiol 31:809–816

    Article  PubMed  CAS  Google Scholar 

  16. Kiryu S, Akai H, Nojima M et al (2017) Impact of hepatocellular carcinoma heterogeneity on computed tomography as a prognostic indicator. Sci Rep 7:12689

  17. Yasaka K, Akai H, Nojima M et al (2017) Quantitative computed tomography texture analysis for estimating histological subtypes of thymic epithelial tumors. Eur J Radiol 92:84–92

    Article  PubMed  Google Scholar 

  18. Ganeshan B, Abaleke S, Young RCD et al (2010) Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage. Cancer Imaging 10:137–143

    Article  PubMed  PubMed Central  Google Scholar 

  19. Wu Z, Matsui O, Kitao A et al (2015) Hepatitis C related chronic liver cirrhosis: feasibility of texture analysis of MR images for classification of fibrosis stage and necroinflammatory activity grade. PLoS One 10:e0118297

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Kleiner DE, Brunt EM, Van Natta M et al (2005) Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology 41:1313–1321

    Article  PubMed  Google Scholar 

  21. Suzuki A, Angulo P, Lymp J et al (2005) Hyaluronic acid, an accurate serum marker for severe hepatic fibrosis in patients with non-alcoholic fatty liver disease. Liver Int 25:779–786

    Article  PubMed  CAS  Google Scholar 

  22. Pavlides M, Banerjee R, Tunnicliffe EM et al (2017) Multiparametric magnetic resonance imaging for the assessment of non-alcoholic fatty liver disease severity. Liver Int 37:1065–1073

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Valls C, Iannacconne R, Alba E et al (2006) Fat in the liver: diagnosis and characterization. Eur Radiol 16:2292–2308

    Article  PubMed  Google Scholar 

  24. Ahn SJ, Kim JH, Park SJ, Han JK (2016) Prediction of the therapeutic response after FOLFOX and FOLFIRI treatment for patients with liver metastasis from colorectal cancer using computerized CT texture analysis. Eur J Radiol 85:1867–1874

    Article  PubMed  Google Scholar 

  25. Nketiah G, Elschot M, Kim E et al (2017) T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results. Eur Radiol 27:3050–3059

    Article  PubMed  Google Scholar 

  26. Molina D, Pérez-Beteta J, Luque B et al (2016) Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival. Br J Radiol 89:20160242

    Article  PubMed Central  Google Scholar 

  27. Kaneda H, Hashimoto E, Yatsuji S et al (2006) Hyaluronic acid levels can predict severe fibrosis and platelet counts can predict cirrhosis in patients with nonalcoholic fatty liver disease. J Gastroenterol Hepatol 21:1459–1465

    PubMed  CAS  Google Scholar 

  28. Mackin D, Fave X, Zhang L et al (2015) Measuring computed tomography scanner variability of radiomics features. Investig Radiol 50:757–765

    Article  Google Scholar 

  29. Mahmoud-Ghoneim D, Toussaint G, Constans JM, De Certaines JD (2003) Three dimensional texture analysis in MRI: a preliminary evaluation in gliomas. Magn Reson Imaging 21:983–987

    Article  PubMed  Google Scholar 

  30. Park HJ, Kim JH, Choi SY et al (2017) Prediction of therapeutic response of hepatocellular carcinoma to transcatheter arterial chemoembolization based on pretherapeutic dynamic CT and textural findings. AJR Am J Roentgenol 209:W211–W220

    Article  PubMed  Google Scholar 

  31. Chen W, Giger ML, Li H et al (2007) Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images. Magn Reson Med 58:562–571

    Article  PubMed  Google Scholar 

  32. Nelson DA, Tan TT, Rabson AB et al (2004) Hypoxia and defective apoptosis drive genomic instability and tumorigenesis. Genes Dev 18:2095–2107

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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Funding

We received no financial support for this study.

Author information

Author notes

  1. Shotaro Naganawa and Shigeru Kiryu contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan

    Shotaro Naganawa & Osamu Abe

  2. Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan

    Kenichiro Enooku & Ryosuke Tateishi

  3. Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1, Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan

    Hiroyuki Akai & Koichiro Yasaka

  4. Department of Pathology, Kyorin University, School of Medicine, 6-20-2, Shinkawa, Mitaka-shi, Tokyo, 181-0004, Japan

    Junji Shibahara

  5. Department of Pathology, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan

    Tetsuo Ushiku

  6. International University of Health and Welfare, 2600-1 kitakanamaru, Otawara, Tochigi, 324-8501, Japan

    Kuni Ohtomo

  7. Department of Radiology, International University of Health and Welfare Hospital, 537-3, Iguchi, Nasushiobara, Tochigi, 329-2763, Japan

    Shigeru Kiryu

Authors
  1. Shotaro Naganawa
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  2. Kenichiro Enooku
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  3. Ryosuke Tateishi
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  4. Hiroyuki Akai
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  5. Koichiro Yasaka
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  6. Junji Shibahara
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  7. Tetsuo Ushiku
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  8. Osamu Abe
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  9. Kuni Ohtomo
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  10. Shigeru Kiryu
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Corresponding author

Correspondence to Shigeru Kiryu.

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Guarantor

The scientific guarantor of this publication is Shigeru Kiryu.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

Authors S.N. and S.K. have significant statistical expertise.

Informed consent

The need for written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic study

• performed at one institution

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Naganawa, S., Enooku, K., Tateishi, R. et al. Imaging prediction of nonalcoholic steatohepatitis using computed tomography texture analysis. Eur Radiol 28, 3050–3058 (2018). https://doi.org/10.1007/s00330-017-5270-5

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  • DOI: https://doi.org/10.1007/s00330-017-5270-5

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