Advertisement

European Radiology

, Volume 29, Issue 3, pp 1067–1073 | Cite as

CT texture analysis of pancreatic cancer

  • Kumar SandrasegaranEmail author
  • Yuning Lin
  • Michael Asare-Sawiri
  • Tai Taiyini
  • Mark Tann
Gastrointestinal

Abstract

Objectives

We investigated the value of CT texture analysis (CTTA) in predicting prognosis of unresectable pancreatic cancer.

Methods

Sixty patients with unresectable pancreatic cancers at presentation were enrolled for post-processing with CTTA using commercially available software (TexRAD Ltd, Cambridge, UK). The largest cross-section of the tumour on axial CT was chosen to draw a region-of-interest. CTTA parameters (mean value of positive pixels (MPP), kurtosis, entropy, skewness), arterial and venous invasion, metastatic disease and tumour size were correlated with overall and progression-free survivals.

Results

The median overall and progression-free survivals of cohort were 13.3 and 7.8 months, respectively. On multivariate Cox proportional hazard regression analysis, presence of metastatic disease at presentation had the highest association with overall survival (p = 0.003–0.05) and progression-free survival (p < 0.001 to p = 0.004). MPP at medium spatial filter was significantly associated with poor overall survival (p = 0.04). On Kaplan–Meier survival analysis of CTTA parameters at medium spatial filter, MPP of more than 31.625 and kurtosis of more than 0.565 had significantly worse overall survival (p = 0.036 and 0.028, respectively).

Conclusions

CTTA features were significantly associated with overall survival in pancreas cancer, particularly in patients with non-metastatic, locally advanced disease.

Key Points

• CT texture analysis is easy to perform on contrast-enhanced CT.

• CT texture analysis can determine prognosis in patients with unresectable pancreas cancer.

• The best predictors of poor prognosis were high kurtosis and MPP.

Keywords

Tomography, X-Ray Computed Pancreas cancer Survival analysis Neoplasm invasion Neoplasm metastases 

Abbreviations

CTTA

CT texture analysis

MPP

Mean value of positive pixels

NCCN

National Comprehensive Cancer Network in USA

SSF

Spatial scaling factor

Notes

Funding

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

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Kumaresan Sandrasegaran, M.D.

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

A professor of Biostatistics (Dr Yan Tong, PhD) was consulted for specialist advice.

Informed consent

Written informed consent was waived by the institutional review board.

Ethical approval

Institutional review board approval was obtained.

Methodology

• Retrospective

• Case-control study

• Performed at one institution

References

  1. 1.
    Brosens LA, Hackeng WM, Offerhaus GJ, Hruban RH, Wood LD (2015) Pancreatic adenocarcinoma pathology: changing "landscape". J Gastrointest Oncol 6:358–374Google Scholar
  2. 2.
    Ethun CG, Kooby DA (2016) The importance of surgical margins in pancreatic cancer. J Surg Oncol 113:283–288Google Scholar
  3. 3.
    Hidalgo M, Cascinu S, Kleeff J et al (2015) Addressing the challenges of pancreatic cancer: future directions for improving outcomes. Pancreatology 15:8–18Google Scholar
  4. 4.
    Gall TM, Tsakok M, Wasan H, Jiao LR (2015) Pancreatic cancer: current management and treatment strategies. Postgrad Med J 91:601–607Google Scholar
  5. 5.
    Vincent A, Herman J, Schulick R, Hruban RH, Goggins M (2011) Pancreatic cancer. Lancet 378:607–620Google Scholar
  6. 6.
    Tempero MA, Malafa MP, Al-Hawary M et al (2017) Pancreatic adenocarcinoma, version 2.2017, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw 15:1028–1061Google Scholar
  7. 7.
    Nelson DA, Tan TT, Rabson AB, Anderson D, Degenhardt K, White E (2004) Hypoxia and defective apoptosis drive genomic instability and tumorigenesis. Genes Dev 18:2095–2107Google Scholar
  8. 8.
    Marusyk A, Polyak K (2010) Tumor heterogeneity: causes and consequences. Biochim Biophys Acta 1805:105–117Google Scholar
  9. 9.
    Easwaran H, Tsai HC, Baylin SB (2014) Cancer epigenetics: tumor heterogeneity, plasticity of stem-like states, and drug resistance. Mol Cell 54:716–727Google Scholar
  10. 10.
    An FQ, Matsuda M, Fujii H et al (2001) Tumor heterogeneity in small hepatocellular carcinoma: analysis of tumor cell proliferation, expression and mutation of p53 AND beta-catenin. Int J Cancer 93:468–474Google Scholar
  11. 11.
    Yip C, Davnall F, Kozarski R et al (2015) Assessment of changes in tumor heterogeneity following neoadjuvant chemotherapy in primary esophageal cancer. Dis Esophagus 28:172–179Google Scholar
  12. 12.
    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 270:141–148Google Scholar
  13. 13.
    Gerlinger M, Rowan AJ, Horswell S et al (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366:883–892Google Scholar
  14. 14.
    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 82:342–348Google Scholar
  15. 15.
    Yip C, Tacelli N, Remy-Jardin M et al (2015) Imaging tumor response and tumoral heterogeneity in non-small cell lung cancer treated with antiangiogenic therapy: comparison of the prognostic ability of RECIST 1.1, an alternate method (Crabb), and image heterogeneity analysis. J Thorac Imaging 30:300–307Google Scholar
  16. 16.
    Ganeshan B, Burnand K, Young R, Chatwin C, Miles K (2011) Dynamic contrast-enhanced texture analysis of the liver: initial assessment in colorectal cancer. Invest Radiol 46:160–168Google Scholar
  17. 17.
    Davnall F, Yip CS, Ljungqvist G et al (2012) Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging 3:573–589Google Scholar
  18. 18.
    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 266:177–184Google Scholar
  19. 19.
    Bayanati H, E Thornhill R, Souza CA et al (2015) Quantitative CT texture and shape analysis: can it differentiate benign and malignant mediastinal lymph nodes in patients with primary lung cancer? Eur Radiol 25:480–487Google Scholar
  20. 20.
    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 22:796–802Google Scholar
  21. 21.
    Ganeshan B, Skogen K, Pressney I, Coutroubis D, Miles K (2012) Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival. Clin Radiol 67:157–164Google Scholar
  22. 22.
    Zhang H, Graham CM, Elci O et al (2013) Locally advanced squamous cell carcinoma of the head and neck: CT texture and histogram analysis allow independent prediction of overall survival in patients treated with induction chemotherapy. Radiology 269:801–809Google Scholar
  23. 23.
    Ahn SY, Park CM, Park SJ et al (2015) Prognostic value of computed tomography texture features in non-small cell lung cancers treated with definitive concomitant chemoradiotherapy. Invest Radiol 50:719–725Google Scholar
  24. 24.
    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 261:165–171Google Scholar
  25. 25.
    Tian F, Hayano K, Kambadakone AR, Sahani DV (2015) Response assessment to neoadjuvant therapy in soft tissue sarcomas: using CT texture analysis in comparison to tumor size, density, and perfusion. Abdom Imaging 40:1705–1712Google Scholar
  26. 26.
    Sandrasegaran KSA, Deng Y, Samuel A et al (2017) Usefulness of CT texture analysis in characterizing renal cancers. Radiological Society of North America RSNA, Chicago, IllGoogle Scholar
  27. 27.
    Ganeshan B, Miles KA (2013) Quantifying tumour heterogeneity with CT. Cancer Imaging 13:140–149Google Scholar
  28. 28.
    Miles KA, Ganeshan B, Hayball MP (2013) CT texture analysis using the filtration-histogram method: what do the measurements mean? Cancer Imaging 13:400–406Google Scholar
  29. 29.
    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 10:137–143Google Scholar
  30. 30.
    Sasaguri K, Takahashi N, Gomez-Cardona D et al (2015) Small (<4 cm) renal mass: differentiation of oncocytoma from renal cell carcinoma on biphasic contrast-enhanced CT. AJR Am J Roentgenol 205:999–1007Google Scholar
  31. 31.
    Aickin M, Gensler H (1996) Adjusting for multiple testing when reporting research results: the Bonferroni vs Holm methods. Am J Public Health 86:726–728Google Scholar
  32. 32.
    Ganeshan B, Miles KA, Young RC, Chatwin CR (2007) Hepatic entropy and uniformity: additional parameters that can potentially increase the effectiveness of contrast enhancement during abdominal CT. Clin Radiol 62:761–768Google Scholar
  33. 33.
    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 40:2331–2337Google Scholar
  34. 34.
    Hayano K, Tian F, Kambadakone AR et al (2015) Texture analysis of non-contrast-enhanced computed tomography for assessing angiogenesis and survival of soft tissue sarcoma. J Comput Assist Tomogr 39:607–612Google Scholar
  35. 35.
    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 posttherapy images using CT texture analysis. AJR Am J Roentgenol 205:W283–W293Google Scholar
  36. 36.
    Eilaghi A, Baig S, Zhang Y et al (2017) CT texture features are associated with overall survival in pancreatic ductal adenocarcinoma - a quantitative analysis. BMC Med Imaging 17:38Google Scholar
  37. 37.
    Hodgdon T, McInnes MD, Schieda N, Flood TA, Lamb L, Thornhill RE (2015) Can quantitative CT texture analysis be used to differentiate fat-poor renal angiomyolipoma from renal cell carcinoma on unenhanced CT images? Radiology 276:787–796Google Scholar
  38. 38.
    Schieda N, Thornhill RE, Al-Subhi M et al (2015) Diagnosis of sarcomatoid renal cell carcinoma with CT: evaluation by qualitative imaging features and texture analysis. AJR Am J Roentgenol 204:1013–1023Google Scholar
  39. 39.
    Takahashi N, Leng S, Kitajima K et al (2015) Small (< 4 cm) renal masses: differentiation of angiomyolipoma without visible fat from renal cell carcinoma using unenhanced and contrast-enhanced CT. AJR Am J Roentgenol 205:1194–1202Google Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of RadiologyIndiana University School of MedicineIndianapolisUSA
  2. 2.Department of Medical ImagingFuzhou General HospitalFuzhouChina
  3. 3.Hope Radiation CancerPanama CityUSA

Personalised recommendations