Accuracy of liver surface nodularity quantification on MDCT for staging hepatic fibrosis in patients with hepatitis C virus

  • Meghan G. Lubner
  • Daniel Jones
  • Adnan Said
  • John Kloke
  • Scott Lee
  • Perry J. Pickhardt
Article
  • 28 Downloads

Abstract

Purpose

To evaluate semi-automated measurement of liver surface nodularity (LSN) on MDCT in a cause-specific cohort of patients with chronic hepatitis C virus infection (HCV) for identification of hepatic fibrosis (stages F0–4).

Methods

MDCT scans in patients with known HCV were evaluated with an independently validated, semi-automated LSN measurement tool. Consecutive LSN measurements along the anterior liver surface were performed to derive mean LSN scores. Scores were compared with METAVIR fibrosis stage (F0–4). Fibrosis stages F0–3 were based on biopsy results within 1 year of CT. Most patients with cirrhosis (F4) also had biopsy within 1 year; the remaining cases had unequivocal clinical/imaging evidence of cirrhosis and biopsy was not indicated.

Results

288 patients (79F/209M; mean age, 49.7 years) with known HCV were stratified based on METAVIR fibrosis stage: F0 (n = 43), F1 (n = 29), F2 (n = 53), F3 (n = 37), and F4 (n = 126). LSN scores increased with increasing fibrosis (mean: F0 = 2.3 ± 0.2, F1 = 2.4 ± 0.3, F2 = 2.6 ± 0.5, F3 = 2.9 ± 0.6, F4 = 3.8 ± 1.0; p < 0.001). For identification of significant fibrosis (≥ F2), advanced fibrosis (≥ F3), and cirrhosis (≥ F4), the ROC AUCs were 0.88, 0.89, and 0.90, respectively. The sensitivity and specificity for significant fibrosis (≥ F2) using LSN threshold of 2.80 were 0.68 and 0.97; for advanced fibrosis (≥ F3; threshold = 2.77) were 0.83 and 0.85; and for cirrhosis (≥ F4, LSN threshold = 2.9) were 0.90 and 0.80.

Conclusion

Liver surface nodularity assessment at MDCT allows for accurate discrimination of intermediate stages of hepatic fibrosis in a cause-specific cohort of patients with HCV, particularly at more advanced levels.

Keywords

Computed tomography Liver surface nodularity Hepatitis C virus Liver fibrosis 

Notes

Compliance with ethical standards

Funding

No funding supported this work.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The need for informed consent was waived.

Disclosures

MGL: Grant funding—Philips, Ethicon. PJP: Co-founder, VirtuoCTC, Advisor to Check-Cap, Shareholder in Cellectar, Elucent, SHINE and for other authors there are no relevant disclosures.

References

  1. 1.
    Freiman JM, Tran TM, Schumacher SG, et al. (2016) Hepatitis C core antigen testing for diagnosis of hepatitis C virus infection: a systematic review and meta-analysis. Ann Intern Med 165:345–355CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    WHO (2017) Hepatitis C virus fact sheet. Accessed 17 May 2017Google Scholar
  3. 3.
    Gower E, Estes C, Blach S, Razavi-Shearer K, Razavi H (2014) Global epidemiology and genotype distribution of the hepatitis C virus infection. J Hepatol 61:S45–S57CrossRefPubMedGoogle Scholar
  4. 4.
    Nuno Solinis R, Arratibel Ugarte P, Rojo A, Sanchez Gonzalez Y (2016) Value of treating all stages of chronic hepatitis C: a comprehensive review of clinical and economic evidence. Infect Dis Ther 5:491–508CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Friedrich-Rust M, Nierhoff J, Lupsor M, et al. (2012) Performance of acoustic radiation force impulse imaging for the staging of liver fibrosis: a pooled meta-analysis. J Viral Hepat 19:E212–E219CrossRefPubMedGoogle Scholar
  6. 6.
    Friedrich-Rust M, Ong M-F, Martens S, et al. (2008) Performance of transient elastography for the staging of liver fibrosis: a meta-analysis. Gastroenterology 134:960–974CrossRefPubMedGoogle Scholar
  7. 7.
    Singh S, Venkatesh SK, Wang Z, et al. (2015) Diagnostic performance of magnetic resonance elastography in staging liver fibrosis: a systematic review and meta-analysis of individual participant data. Clin Gastroenterol Hepatol 13:440–451CrossRefPubMedGoogle Scholar
  8. 8.
    Talwalkar JA, Kurtz DM, Schoenleber SJ, West CP, Montori VM (2007) Utrasound-based transient elastography for the detection of hepatic fibrosis: systematic review and meta-analysis. Clin Gastroenterol Hepatol 5:1214–1220CrossRefPubMedGoogle Scholar
  9. 9.
    Wang Q-B, Zhu H, Liu H-L, Zhang B (2012) Performance of magnetic resonance elastography and diffusion-weighted imaging for the staging of hepatic fibrosis: a meta-analysis. Hepatology 56:239–247CrossRefPubMedGoogle Scholar
  10. 10.
    Castera L, Vergniol J, Foucher J, et al. (2005) Prospective comparison of transient elastography, fibrotest, APRI, and liver biopsy for the assessment of fibrosis in chronic hepatitis C. Gastroenterology 128:343–350CrossRefPubMedGoogle Scholar
  11. 11.
    Foucher J, Chanteloup E, Vergniol J, et al. (2006) Diagnosis of cirrhosis by transient elastography (FibroScan): a prospective study. Gut 55:403–408CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Yin M, Glaser KJ, Talwalkar JA, et al. (2016) Hepatic MR elastography: clinical performance in a series of 1377 consecutive examinations. Radiology 278:114–124CrossRefPubMedGoogle Scholar
  13. 13.
    Tang A, Cloutier G, Szeverenyi NM, Sirlin CB (2015) Ultrasound elastography and MR elastography for assessing liver fibrosis: part 2, diagnostic performance, confounders, and future directions. Am J Roentgenol 205:33–40CrossRefGoogle Scholar
  14. 14.
    Wagner M, Corcuera-Solano I, Lo G, et al. (2017) Technical failure of MR elastography examinations of the liver: experience from a large single-center study. Radiology.  https://doi.org/10.1148/radiol.2016160863:160863 Google Scholar
  15. 15.
    Petitclerc L, Sebastiani G, Gilbert G, Cloutier G, Tang A (2016) Liver fibrosis: review of current imaging and MRI quantification techniques. J Magn Reson Imaging.  https://doi.org/10.1002/jmri.25550 PubMedGoogle Scholar
  16. 16.
    Furusato Hunt OM, Lubner MG, Ziemlewicz TJ, Munoz Del Rio A, Pickhardt PJ (2016) The liver segmental volume ratio for noninvasive detection of cirrhosis: comparison with established linear and volumetric measures. J Comput Assist Tomogr 40:478–484CrossRefPubMedGoogle Scholar
  17. 17.
    Honda H, Onitsuka H, Masuda K, et al. (1990) Chronic liver disease: value of volumetry of liver and spleen with computed tomography. Radiat Med 8:222–226PubMedGoogle Scholar
  18. 18.
    Smith AD, Branch CR, Zand K, et al. (2016) Liver surface nodularity quantification from routine CT images as a biomarker for detection and evaluation of cirrhosis. Radiology 280:771–781CrossRefPubMedGoogle Scholar
  19. 19.
    Zhou X, Lu T, Wei Y, Chen X (2007) Liver volume variation in patients with virus-induced cirrhosis: findings on MDCT. AJR 189:W153–W159CrossRefPubMedGoogle Scholar
  20. 20.
    Smith AD, Zand KA, Florez E, et al. (2016) Liver surface nodularity score allows prediction of cirrhosis decompensation and death. Radiology.  https://doi.org/10.1148/radiol.2016160799:160799 Google Scholar
  21. 21.
    Pickhardt PJ, Malecki K, Hunt OF, et al. (2017) Hepatosplenic volumetric assessment at MDCT for staging liver fibrosis. Eur Radiol 27:3060–3068CrossRefPubMedGoogle Scholar
  22. 22.
    Pickhardt PJ, Malecki K, Kloke J, Lubner MG (2016) Accuracy of liver surface nodularity quantification on MDCT as a noninvasive biomarker for staging hepatic fibrosis. AJR 207:1194–1199CrossRefPubMedGoogle Scholar
  23. 23.
    Lo GC, Besa C, King MJ, et al. (2017) Feasibility and reproducibility of liver surface nodularity quantification for the assessment of liver cirrhosis using CT and MRI. Eur J Radiol Open 4:95–100CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Smith AD, Branch CR, Zand K, et al. (2016) Liver surface nodularity quantification from routine computed tomography images as a biomarker for detection and evaluation of cirrhosis. Radiology 280:771–781CrossRefPubMedGoogle Scholar
  25. 25.
    Bedossa P, Poynard T (1996) An algorithm for the grading of activity in chronic hepatitis C. The METAVIR cooperative study group. Hepatology 24:289–293CrossRefPubMedGoogle Scholar
  26. 26.
    Martinez SM, Crespo G, Navasa M, Forns X (2011) Noninvasive assessment of liver fibrosis. Hepatology 53:325–335CrossRefPubMedGoogle Scholar
  27. 27.
    Daginawala N, Li B, Buch K, et al. (2016) Using texture analyses of contrast enhanced CT to assess hepatic fibrosis. Eur J Radiol 85:511–517CrossRefPubMedGoogle Scholar
  28. 28.
    Lubner MG, Malecki K, Kloke J, Ganeshan B, Pickhardt PJ (2017) Texture analysis of the liver at MDCT for assessing hepatic fibrosis. Abdom Radiol 42:2069–2078CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Meghan G. Lubner
    • 1
  • Daniel Jones
    • 1
  • Adnan Said
    • 2
  • John Kloke
    • 1
  • Scott Lee
    • 1
  • Perry J. Pickhardt
    • 1
  1. 1.Department of RadiologyUniversity of Wisconsin School of Medicine and Public HealthMadisonUSA
  2. 2.Division of Gastroenterology/Hepatology, Department of Internal MedicineUniversity of Wisconsin School of Medicine and Public HealthMadisonUSA

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