Skip to main content

Advertisement

SpringerLink
Log in

Feasibility of computed tomography texture analysis of hepatic fibrosis using dual-energy spectral detector computed tomography

  • Original Article
  • Published:
Japanese Journal of Radiology Aims and scope Submit manuscript

Abstract

Purpose

To evaluate feasibility of computer tomography texture analysis (CTTA) at different energy level using dual-energy spectral detector CT for liver fibrosis.

Materials and methods

Eighty-seven patients who underwent a spectral CT examination and had a reference standard of liver fibrosis (histopathologic findings, n = 61, or clinical findings for normal, n = 26) were included. Mean gray-level intensity, mean number of positive pixels (MPP), entropy, skewness, and kurtosis using commercially available software (TexRAD) were compared at different energy levels. Optimal CTTA parameter cutoffs to diagnose liver fibrosis were evaluated. CTTA parameters at different energy levels correlated with liver fibrosis. The association of CTTA parameters with energy level was evaluated.

Results

Mean gray-level intensity, skewness, kurtosis, and entropy showed significant differences between patients with and without clinically significant hepatic fibrosis (P < 0.05). Mean gray-level intensity at 50 keV was significantly positively correlated with liver fibrosis (ρ = 0.502, P < 0.001). To diagnose stages F2–F4, entropy and mean gray-level intensity at low keV level showed the largest area under the curve (AUC; 0.79 and 0.79). Estimated marginal means (EMMs) of mean gray-level intensity showed prominent differences at low energy levels.

Conclusion

CTTA parameters from different keV levels demonstrated meaningful accuracy for diagnosis of liver fibrosis or clinically significant hepatic fibrosis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Heidelbaugh JJ, Bruderly M. Cirrhosis and chronic liver failure: part I. Diagnosis and evaluation. Am Fam Physician. 2006;74:756–62.

    PubMed  Google Scholar 

  2. Lee SS, Byoun YS, Jeong SH, Kim YM, Gil H, Min BY, et al. Type and cause of liver disease in korea: single-center experience, 2005–2010. Clin Mol Hepatol. 2012;18:309–15.

    PubMed  PubMed Central  Google Scholar 

  3. World Health Organization. Global hepatitis report 2017. Washington: World Health Organization; 2017.

    Google Scholar 

  4. Friedman SL. Liver fibrosis—from bench to bedside. J Hepatol. 2003;38:38–533.

    Google Scholar 

  5. Horowitz JM, Venkatesh SK, Ehman RL, Jhaveri K, Kamath P, Ohliger MA, et al. Evaluation of hepatic fibrosis: a review from the society of abdominal radiology disease focus panel. Abdom Radiol (New York). 2017;42:2037–53.

    PubMed  PubMed Central  Google Scholar 

  6. Akkaya HE, Erden A, Kuru Oz D, Unal S, Erden I. Magnetic resonance elastography: basic principles, technique, and clinical applications in the liver. Diagn Interv Radiol. 2018;24:328–35.

    PubMed  PubMed Central  Google Scholar 

  7. Bravo AA, Sheth SG, Chopra S. Liver biopsy. N Engl J Med. 2001;344:495–500.

    CAS  PubMed  Google Scholar 

  8. Park HJ, Lee SS, Park B, Yun J, Sung YS, Shim WH, et al. Radiomics analysis of gadoxetic acid-enhanced MRI for staging liver fibrosis. Radiology. 2019;290:380–7.

    PubMed  Google Scholar 

  9. Low G, Kruse SA, Lomas DJ. General review of magnetic resonance elastography. World J Radiol. 2016;8:59–72.

    PubMed  PubMed Central  Google Scholar 

  10. Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ. Ct texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics. 2017;37:1483–503.

    PubMed  Google Scholar 

  11. Hanania AN, Bantis LE, Feng Z, Wang H, Tamm EP, Katz MH, et al. Quantitative imaging to evaluate malignant potential of ipmns. Oncotarget. 2016;7:85776–84.

    PubMed  PubMed Central  Google Scholar 

  12. Hu Y, Liang Z, Song B, Han H, Pickhardt PJ, Zhu W, et al. Texture feature extraction and analysis for polyp differentiation via computed tomography colonography. IEEE Trans Med Imaging. 2016;35:1522–31.

    PubMed  PubMed Central  Google Scholar 

  13. Raman SP, Chen Y, Schroeder JL, Huang P, Fishman EK. Ct texture analysis of renal masses: pilot study using random forest classification for prediction of pathology. Acad Radiol. 2014;21:1587–96.

    PubMed  PubMed Central  Google Scholar 

  14. Raman SP, Schroeder JL, Huang P, Chen Y, Coquia SF, Kawamoto S, et al. Preliminary data using computed tomography texture analysis for the classification of hypervascular liver lesions: generation of a predictive model on the basis of quantitative spatial frequency measurements—a work in progress. J Comput Assist Tomogr. 2015;39:383–95.

    PubMed  Google Scholar 

  15. Lubner MG, Malecki K, Kloke J, Ganeshan B, Pickhardt PJ. Texture analysis of the liver at mdct for assessing hepatic fibrosis. Abdom Radiol (New York). 2017;42:2069–78.

    Google Scholar 

  16. Baliyan V, Kordbacheh H, Parameswaran B, Ganeshan B, Sahani D, Kambadakone A. Virtual monoenergetic imaging in rapid kvp-switching dual-energy ct (dect) of the abdomen: impact on ct texture analysis. Abdom Radiol (New York). 2018;43:2693–701.

    Google Scholar 

  17. Coursey CA, Nelson RC, Boll DT, Paulson EK, Ho LM, Neville AM, et al. Dual-energy multidetector ct: how does it work, what can it tell us, and when can we use it in abdominopelvic imaging? Radiographics. 2010;30:1037–55.

    PubMed  Google Scholar 

  18. Goodman ZD. Grading and staging systems for inflammation and fibrosis in chronic liver diseases. J Hepatol. 2007;47:598–607.

    PubMed  Google Scholar 

  19. Chung SR, Lee SS, Kim N, Yu ES, Kim E, Kuhn B, et al. Intravoxel incoherent motion MRI for liver fibrosis assessment: a pilot study. Acta Radiol. 2015;56:1428–36.

    PubMed  Google Scholar 

  20. Ganeshan B, Miles KA, Young RC, Chatwin CR. Hepatic enhancement in colorectal cancer: texture analysis correlates with hepatic hemodynamics and patient survival. Acad Radiol. 2007;14:1520–30.

    PubMed  Google Scholar 

  21. Ganeshan B, Miles KA, Young RC, Chatwin CR. In search of biologic correlates for liver texture on portal-phase ct. Acad Radiol. 2007;14:1058–68.

    PubMed  Google Scholar 

  22. Bandula S, Punwani S, Rosenberg WM, Jalan R, Hall AR, Dhillon A, et al. Equilibrium contrast-enhanced ct imaging to evaluate hepatic fibrosis: Initial validation by comparison with histopathologic sampling. Radiology. 2015;275:136–43.

    PubMed  Google Scholar 

  23. Ganeshan B, Miles KA, Young RC, Chatwin CR. Texture analysis in non-contrast enhanced ct: impact of malignancy on texture in apparently disease-free areas of the liver. Eur J Radiol. 2009;70:101–10.

    PubMed  Google Scholar 

  24. Daginawala N, Li B, Buch K, Yu H, Tischler B, Qureshi MM, et al. Using texture analyses of contrast enhanced ct to assess hepatic fibrosis. Eur J Radiol. 2016;85:511–7.

    PubMed  Google Scholar 

  25. Lubner MG, Jones D, Kloke J, Said A, Pickhardt PJ. Ct texture analysis of the liver for assessing hepatic fibrosis in patients with hepatitis c virus. Br J Radiol. 2019;92:20180153.

    PubMed  Google Scholar 

  26. Varghese BA, Cen SY, Hwang DH, Duddalwar VA. Texture analysis of imaging: what radiologists need to know. Am J Roentgenol. 2019;212:520–8.

    Google Scholar 

  27. Ganeshan B, Abaleke S, Young RC, Chatwin CR, Miles KA. 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. 2010;10:137–43.

    PubMed  PubMed Central  Google Scholar 

  28. Goh V, Ganeshan B, Nathan P, Juttla JK, Vinayan A, Miles KA. Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: Ct texture as a predictive biomarker. Radiology. 2011;261:165–71.

    PubMed  Google Scholar 

  29. Miles KA, Ganeshan B, Hayball MP. Ct texture analysis using the filtration-histogram method: what do the measurements mean? Cancer Imaging. 2013;13:400–6.

    PubMed  PubMed Central  Google Scholar 

  30. Rassouli N, Etesami M, Dhanantwari A, Rajiah P. Detector-based spectral ct with a novel dual-layer technology: principles and applications. Insights Imaging. 2017;8:589–98.

    PubMed  PubMed Central  Google Scholar 

  31. Rassouli N, Chalian H, Rajiah P, Dhanantwari A, Landeras L. Assessment of 70-kev virtual monoenergetic spectral images in abdominal ct imaging: a comparison study to conventional polychromatic 120-kvp images. Abdom Radiol (New York). 2017;42:2579–86.

    Google Scholar 

  32. Baron RL, Gore R. Diffuse liver disease. Textbook of gastrointestinal radiology. 2nd ed. Philadelphia: Saunders; 2000. p. 1590–1638.

    Google Scholar 

  33. Ohkoshi S, Hirono H, Watanabe K, Hasegawa K, Kamimura K, Yano M. Natural regression of fibrosis in chronic hepatitis b. World J Gastroenterol. 2016;22:5459–66.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Ros PR, Mortele KJ. Diffuse liver disease. Clin Liver Dis. 2002;6:181–201.

    PubMed  Google Scholar 

  35. Hernandez-Gea V, Friedman SL. Pathogenesis of liver fibrosis. Annu Rev Pathol. 2011;6:425–56.

    CAS  PubMed  Google Scholar 

  36. Sofue K, Tsurusaki M, Mileto A, Hyodo T, Sasaki K, Nishii T, et al. Dual-energy computed tomography for non-invasive staging of liver fibrosis: accuracy of iodine density measurements from contrast-enhanced data. Hepatol Res. 2018;48:1008–199.

    PubMed  Google Scholar 

  37. Murray N, Darras KE, Walstra FE, Mohammed MF. Dual-energy ct in evaluation of the acute abdomen. Radiographics. 2019;39:264–86.

    PubMed  Google Scholar 

  38. Choi KJ, Jang JK, Lee SS, Sung YS, Shim WH, Kim HS. Development and validation of a deep learning system for staging liver fibrosis by using contrast agent-enhanced ct images in the liver. Radiology. 2018;289:688–97.

    PubMed  Google Scholar 

  39. Choi IY, Yeom SK. Feasibility of using computed tomography texture analysis parameters as imaging biomarkers for predicting risk grade of gastrointestinal stromal tumors: comparison with visual inspection. Abdom Radiol (NY). 2019;44:2346–56.

    PubMed  Google Scholar 

  40. Ng F, Kozarski R, Ganeshan B, Goh V. Assessment of tumor heterogeneity by ct texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis? Eur J Radiol. 2013;82:342–8.

    PubMed  Google Scholar 

  41. Ito E, Sato K, Yamamoto R, Sakamoto K, Urakawa H, Yoshimitsu K. Usefulness of iodine-blood material density images in estimating degree of liver fibrosis by calculating extracellular volume fraction obtained from routine dual-energy liver ct protocol equilibrium phase data: preliminary experience. Jpn J Radiol. 2020;38:365–73.

    CAS  PubMed  Google Scholar 

  42. Shinagawa Y, Sakamoto K, Sato K, Ito E, Urakawa H, Yoshimitsu K. Usefulness of new subtraction algorithm in estimating degree of liver fibrosis by calculating extracellular volume fraction obtained from routine liver ct protocol equilibrium phase data: preliminary experience. Eur J Radiol. 2018;103:99–104.

    PubMed  Google Scholar 

Download references

Acknowledgements

This research was supported by Korea University Ansan Hospital Grant (O1801331).

Funding

The authors state that this work has not received any funding.

Author information

Authors and Affiliations

  1. Department of Radiology, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan, 15355, Republic of Korea

    ByukGyung Choi, In Young Choi, Sang Hoon Cha, Suk Keu Yeom, Hwan Hoon Chung & Seung Hwa Lee

  2. Department of Biostatistics, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan, 15355, Republic of Korea

    Jaehyung Cha

  3. Department of Pathology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan, 15355, Republic of Korea

    Ju-Han Lee

Authors
  1. ByukGyung Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. In Young Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sang Hoon Cha
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Suk Keu Yeom
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hwan Hoon Chung
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Seung Hwa Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jaehyung Cha
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ju-Han Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to In Young Choi.

Ethics declarations

Conflict of interest

The scientific guarantor of this publication is Hwan Hoon Chung. 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.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Choi, B., Choi, I.Y., Cha, S.H. et al. Feasibility of computed tomography texture analysis of hepatic fibrosis using dual-energy spectral detector computed tomography. Jpn J Radiol 38, 1179–1189 (2020). https://doi.org/10.1007/s11604-020-01020-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11604-020-01020-5

Keywords

Search

Navigation