Texture analysis of the liver at MDCT for assessing hepatic fibrosis



To evaluate CT texture analysis (CTTA) for staging of hepatic fibrosis (stages F0–F4)


Quantitative texture analysis (QTA) of the liver was performed on abdominal MDCT scans using commercially available software (TexRAD), which uses a filtration-histogram statistic-based technique. Single-slice ROI measurements of the total liver, Couinaud segments IV-VIII, and segments I–III were obtained. CTTA parameters were correlated against fibrosis stage (F0–F4), with biopsy performed within one year for all cases with intermediate fibrosis (F1–F3).


The study cohort consisted of 289 adults (158M/131W; mean age, 51 years), including healthy controls (F0, n = 77), and patients with increasing stages of fibrosis (F1, n = 42; F2 n = 37; F3 n = 53; F4 n = 80). Mean gray-level intensity increased with fibrosis stage, demonstrating an ROC AUC of 0.78 at medium filtration for F0 vs F1-4, with sensitivity and specificity of 74% and 74% at cutoff 0.18. For significant fibrosis (≥F2), mean showed AUCs ranging from 0.71–0.73 across medium- and coarse- filtered textures with sensitivity and specificity of 71% and 68% at cutoff of 0.3, with similar performance also observed for advanced fibrosis (≥F3). Entropy showed a similar trend. Conversely, kurtosis and skewness decreased with increasing fibrosis, particularly in cirrhotic patients. For cirrhosis (≥F4), kurtosis and skewness showed AUCs of 0.86 and 0.87, respectively, at coarse-filtered scale, with skewness showing a sensitivity and specificity of 84% and 75% at cutoff of 1.3.


CTTA may be helpful in detecting the presence of hepatic fibrosis and discriminating between stages of fibrosis, particularly at advanced levels.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5


  1. 1.

    Srinivasa Babu A, Wells ML, Teytelboym OM, et al. (2016) Elastography in chronic liver disease: modalities, techniques, limitations, and future directions. Radiographics 36: 1987–2006

    Article  PubMed  Google Scholar 

  2. 2.

    Prevention CfDCa. Viral Hepatitis. In: Centers for Disease Control and Prevention Website. www.cdc.gov/hepatitis/index.htm. Updated September 18, 2014

  3. 3.

    Afdhal NH, Nunes D. (2004) Evaluation of liver fibrosis: A concise review. Am J Gastroenterol 99:1160–1174

    Article  PubMed  Google Scholar 

  4. 4.

    Martinez SM, Crespo G, Navasa M, Forns X. (2011) Noninvasive assessment of liver fibrosis. Hepatology 53:325–335

    Article  PubMed  Google 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–E219

    CAS  Article  PubMed  Google 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 134:960–974

    Article  PubMed  Google 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 13:440-451

    Article  PubMed  Google 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 5:1214–1220

    Article  PubMed  Google 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 56:239–247

    Article  PubMed  Google 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 128:343–350

    Article  PubMed  Google Scholar 

  11. 11.

    Foucher J, Chanteloup E, Vergniol J, et al. (2006) Diagnosis of cirrhosis by transient elastography (FibroScan): a prospective study. Gut 55:403–408

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Yin M, Glaser KJ, Talwalkar JA, Chen J, Manduca A, Ehman RL. (2016) Hepatic MR elastography: clinical performance in a series of 1377 consecutive examinations. Radiology 278:114–124

    Article  PubMed  Google Scholar 

  13. 13.

    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 160863

  14. 14.

    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 280:771–781

    Article  PubMed  Google Scholar 

  15. 15.

    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 1–6

  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 40:478–484

    Article  PubMed  Google Scholar 

  17. 17.

    Pickhardt PJ MK, Hunt OF, Beaumont C, Kloke J, Ziemlewicz TJ, Lubner MG. (2016) Hepatosplenic volumetric assessment at MDCT for staging liver fibrosis. Eur Radiol 1–9

  18. 18.

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

    Article  Google Scholar 

  19. 19.

    Davnall F, Yip CS, 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 

  20. 20.

    Ganeshan B, Abaleke S, Young RC, Chatwin CR, Miles KA. (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 

  21. 21.

    Ganeshan B, Goh V, Mandeville HC, Ng QS, Hoskin PJ, Miles KA. (2013) Non-small cell lung cancer: histopathologic correlates for texture parameters at CT. Radiology 266:326–336

    Article  PubMed  Google Scholar 

  22. 22.

    Ganeshan B, Panayiotou E, Burnand K, Dizdarevic S, Miles K. (2012) Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival. Eur Radiol 22:796–802

    Article  PubMed  Google Scholar 

  23. 23.

    Lubner MG, Stabo N, Lubner SJ, et al. (2015) CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes. Abdom Imaging

  24. 24.

    Ng F, Ganeshan B, Kozarski R, Miles KA, Goh V. (2013) Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology 266:177–184

    Article  PubMed  Google Scholar 

  25. 25.

    Smith AD, Gray MR, Del Campo SM, et al. (2015) Predicting overall survival in patients with metastatic melanoma on antiangiogenic therapy and RECIST stable disease on initial post therapy images using CT texture analysis. AJR 205:W283–W293

    Article  PubMed  Google Scholar 

  26. 26.

    Stabo N LM, Abel EJ, Munoz del Rio A, Pickhardt PJ. (2014) CT textural analysis of large primary renal cell carcinomas: Tumor heterogeneity correlates with histology and clinical outcomes. In: Society of Abdominal Radiology. San Diego, CA

  27. 27.

    Raman SP, Chen Y, Schroeder JL, Huang P, Fishman EK. (2014) CT Texture analysis of renal masses: pilot study using random forest classification for Prediction of Pathology. Acad Radiol 21:1587–1596

    Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Weiss GJ, Ganeshan B, Miles KA, et al. (2014) Noninvasive image texture analysis differentiates K-ras mutation from pan-Wildtype NSCLC and is prognostic. PLoS ONE 9:e100244

    Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Yip C, Landau D, Kozarski R, et al. (2014) Primary esophageal cancer: heterogeneity as potential prognostic biomarker in patients treated with definitive chemotherapy and radiation therapy. Radiology 270:141–148

    Article  PubMed  Google Scholar 

  30. 30.

    Fujita A, Buch K, Li B, Kawashima Y, Qureshi MM, Sakai O (2016) Difference between HPV-positive and HPV-negative non-oropharyngeal head and neck cancer: Texture analysis features on CT. J Comput Assist Tomogr 40:43–47

    Article  PubMed  Google Scholar 

  31. 31.

    Goh V, Ganeshan B, Nathan P, Juttla JK, Vinayan A, Miles KA. (2011) Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: CT texture as a predictive biomarker. Radiology 261:165–171

    Article  PubMed  Google Scholar 

  32. 32.

    Ozkan E, West A, Dedelow JA, et al. (2015) CT gray-level texture analysis as a quantitative imaging biomarker of epidermal growth factor receptor mutation status in adenocarcinoma of the lung. AJR Am J Roentgenol, 205:1016–1025

    Article  PubMed  Google Scholar 

  33. 33.

    Ginsburg SB, Zhao J, Humphries S et al. (2016) Texture-based quantification of centrilobular emphysema and centrilobular nodularity in longitudinal CT scans of current and former smokers. Acad Radiol 23:1349–1358

    Article  PubMed  Google Scholar 

  34. 34.

    Park HJ, Lee SM, Song JW, et al. (2016) Texture-based automated quantitative assessment of regional patterns on initial CT in patients with idiopathic pulmonary fibrosis: Relationship to decline in forced vital capacity. AJR Am J Roentgenol 1–8

  35. 35.

    Daginawala N, Li B, Buch K, et al. (2016) Using texture analyses of contrast enhanced CT to assess hepatic fibrosis. Eur J Radiol 85:511–17

    Article  PubMed  Google Scholar 

  36. 36.

    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

  37. 37.

    Bedossa P, Poynard T. The METAVIR Cooperative Study Group (1996) An algorithm for the grading of activity in chronic hepatitis C. Hepatology 24:289–93

    CAS  Article  PubMed  Google Scholar 

  38. 38.

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

    Article  PubMed  Google Scholar 

  39. 39.

    Ganeshan B, Miles KA, Young RC, Chatwin CR (2007) In search of biologic correlates for liver texture on portal-phase CT. Acad Radiol 14:1058–1068

    Article  PubMed  Google Scholar 

  40. 40.

    Bandula S, Punwani S, Rosenberg WM, et al. (2015) Equilibrium contrast-enhanced CT imaging to evaluate hepatic fibrosis: initial validation by comparison with histopathologic analysis. Radiology 275:136–43

    Article  PubMed  Google Scholar 

  41. 41.

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

    Article  PubMed  Google Scholar 

  42. 42.

    Ng F, Kozarski R, Ganeshan B, Goh V (2013) 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, 82:342–48

    Article  PubMed  Google Scholar 

  43. 43.

    Miles KA, Ganeshan B, Hayball MP (2013) CT texture analysis using the filtration-histogram method: what do the measurements mean? Cancer Imaging 13:400–06

    Article  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Haralick RM, Shanmugam K, Dinstein I (1973). Textural features for image classification. IEEE Trans Syst Man Cybern 3:610–621

    Article  Google Scholar 

  45. 45.

    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 205:33–40

    Article  Google Scholar 

  46. 46.

    Sieren JC, Smith AR, Thiesse J, et al. (2011). Exploration of the volumetric composition of human lung cancer nodules in correlated histopathology and computed tomography. Lung Cancer (Amsterdam, Netherlands) 74:61–68

    CAS  Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Meghan G. Lubner.

Ethics declarations


No funding was received for this study.

Conflict of interest

Dr. Lubner receives grant funding from Philips and Ethicon. Dr. Ganeshan is Director and part-employed by TexRAD Ltd (part of Feedback Plc, Cambridge, UK). Dr. Pickhardt is co-founder of VirtuoCTC and shareholder in Cellectar Biosciences and SHINE. The other authors declare that they have no conflict of interest.

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. For this type of study, formal consent is not required

Informed consent

The need for informed consent was waived by the IRB.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lubner, M.G., Malecki, K., Kloke, J. et al. Texture analysis of the liver at MDCT for assessing hepatic fibrosis. Abdom Radiol 42, 2069–2078 (2017). https://doi.org/10.1007/s00261-017-1096-5

Download citation


  • CT texture analysis
  • Hepatic fibrosis
  • Liver disease
  • Cirrhosis