European Radiology

, Volume 29, Issue 12, pp 6922–6929 | Cite as

CT texture analysis in the differentiation of major renal cell carcinoma subtypes and correlation with Fuhrman grade

  • Yu Deng
  • Erik Soule
  • Aster Samuel
  • Sakhi Shah
  • Enming Cui
  • Michael Asare-Sawiri
  • Chandru Sundaram
  • Chandana Lall
  • Kumaresan SandrasegaranEmail author



CT texture analysis (CTTA) using filtration-histogram–based parameters has been associated with tumor biologic correlates such as glucose metabolism, hypoxia, and tumor angiogenesis. We investigated the utility of these parameters for differentiation of clear cell from papillary renal cancers and prediction of Fuhrman grade.


A retrospective study was performed by applying CTTA to pretreatment contrast-enhanced CT scans in 290 patients with 298 histopathologically confirmed renal cell cancers of clear cell and papillary types. The largest cross section of the tumor on portal venous phase axial CT was chosen to draw a region of interest. CTTA comprised of an initial filtration step to extract features of different sizes (fine, medium, coarse spatial scales) followed by texture quantification using histogram analysis.


A significant increase in entropy with fine and medium spatial filters was demonstrated in clear cell RCC (p = 0.047 and 0.033, respectively). Area under the ROC curve of entropy at fine and medium spatial filters was 0.804 and 0.841, respectively. An increased entropy value at coarse filter correlated with high Fuhrman grade tumors (p = 0.01). The other texture parameters were not found to be useful.


Entropy, which is a quantitative measure of heterogeneity, is increased in clear cell renal cancers. High entropy is also associated with high-grade renal cancers. This parameter may be considered as a supplementary marker when determining aggressiveness of therapy.

Key points

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

• CT texture analysis may help to separate different types of renal cancers.

• CT texture analysis may enhance individualized treatment of renal cancers.


Cone-beam computerized tomography Image interpretation, computer-assisted Clear cell renal cell carcinoma Papillary renal cell carcinoma Neoplasm grading 



Clear cell renal cell carcinoma


Computerized tomography (CT) texture analysis


Papillary renal cell carcinoma


Renal cell carcinoma

ROC curve

Receiver operating characteristic curve


Spatial scaling factor associated with CTTA



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

Compliance with ethical standards


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

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.


• Retrospective

• Case-control study

• Performed at one institution


  1. 1.
    Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A (2015) Global cancer statistics, 2012. CA Cancer J Clin 65:87–108CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Global Burden of Disease Cancer Collaboration, Fitzmaurice C, Dicker D et al (2015) The global burden of cancer 2013. JAMA Oncol 1:505–527Google Scholar
  3. 3.
    Moch H, Cubilla AL, Humphrey PA, Reuter VE, Ulbright TM (2016) The 2016 WHO classification of tumours of the urinary system and male genital organs-part a: renal, penile, and testicular tumours. Eur Urol 70:93–105CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Hsieh JJ, Purdue MP, Signoretti S et al (2017) Renal cell carcinoma. Nat Rev Dis Primers 3:17009CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Fuhrman SA, Lasky LC, Limas C (1982) Prognostic significance of morphologic parameters in renal cell carcinoma. Am J Surg Pathol 6:655–663CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Young JR, Coy H, Kim HJ et al (2017) Performance of relative enhancement on multiphasic MRI for the differentiation of clear cell renal cell carcinoma (RCC) from papillary and chromophobe RCC subtypes and oncocytoma. AJR Am J Roentgenol 208:812–819CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Mytsyk Y, Dutka I, Borys Y et al (2017) Renal cell carcinoma: applicability of the apparent coefficient of the diffusion-weighted estimated by MRI for improving their differential diagnosis, histologic subtyping, and differentiation grade. Int Urol Nephrol 49:215–224CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Kasoji SK, Chang EH, Mullin LB, Chong WK, Rathmell WK, Dayton PA (2017) A pilot clinical study in characterization of malignant renal-cell carcinoma subtype with contrast-enhanced ultrasound. Ultrason Imaging 39:126–136CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Young JR, Margolis D, Sauk S, Pantuck AJ, Sayre J, Raman SS (2013) Clear cell renal cell carcinoma: discrimination from other renal cell carcinoma subtypes and oncocytoma at multiphasic multidetector CT. Radiology 267:444–453CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Pierorazio PM, Hyams ES, Tsai S et al (2013) Multiphasic enhancement patterns of small renal masses (</=4 cm) on preoperative computed tomography: utility for distinguishing subtypes of renal cell carcinoma, angiomyolipoma, and oncocytoma. Urology 81:1265–1271CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Cheville JC, Lohse CM, Zincke H, Weaver AL, Blute ML (2003) Comparisons of outcome and prognostic features among histologic subtypes of renal cell carcinoma. Am J Surg Pathol 27:612–624CrossRefGoogle Scholar
  12. 12.
    Kim JK, Kim TK, Ahn HJ, Kim CS, Kim KR, Cho KS (2002) Differentiation of subtypes of renal cell carcinoma on helical CT scans. AJR Am J Roentgenol 178:1499–1506CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Miles KA, Ganeshan B, Hayball MP (2013) CT texture analysis using the filtration-histogram method: what do the measurements mean? Cancer Imaging 13:400–406CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    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 266:326–336CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    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 261:165–171CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Yu H, Scalera J, Khalid M et al (2017) Texture analysis as a radiomic marker for differentiating renal tumors. Abdom Radiol (NY).
  17. 17.
    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 21:1587–1596CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Lubner MG, Stabo N, Abel EJ, Del Rio AM, Pickhardt PJ (2016) CT textural analysis of large primary renal cell carcinomas: pretreatment tumor heterogeneity correlates with histologic findings and clinical outcomes. AJR Am J Roentgenol 207:96–105CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Chen F, Huhdanpaa H, Desai B et al (2015) Whole lesion quantitative CT evaluation of renal cell carcinoma: differentiation of clear cell from papillary renal cell carcinoma. Springerplus 4:66CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Bektas CT, Kocak B, Yardimci AH et al (2018) Clear cell renal cell carcinoma: machine learning-based quantitative computed tomography texture analysis for prediction of Fuhrman nuclear grade. Eur Radiol.
  21. 21.
    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 266:177–184CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Ganeshan B, Miles KA (2013) Quantifying tumour heterogeneity with CT. Cancer Imaging 13:140–149CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    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 10:137–143CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    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 270:141–148CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    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 67:157–164CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    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 205:999–1007CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Delahunt B, Eble JN, Egevad L, Samaratunga H (2019) Grading of renal cell carcinoma. Histopathology 74:4–17CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Ding J, Xing Z, Jiang Z et al (2018) CT-based radiomic model predicts high grade of clear cell renal cell carcinoma. Eur J Radiol 103:51–56CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Zhang X, Wang Y, Yang L et al (2018) Delayed enhancement of the peritumoural cortex in clear cell renal cell carcinoma: correlation with Fuhrman grade. Clin Radiol 73:982 e981-982 e987Google Scholar
  30. 30.
    Gu L, Li H, Wang Z et al (2018) A systematic review and meta-analysis of clinicopathologic factors linked to oncologic outcomes for renal cell carcinoma with tumor thrombus treated by radical nephrectomy with thrombectomy. Cancer Treat Rev 69:112–120CrossRefPubMedPubMedCentralGoogle 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 86:726–728CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ (2017) CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics 37:1483–1503CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Sandrasegaran K, Lin Y, Asare-Sawiri M, Taiyini T, Tann M (2019) CT texture analysis of pancreatic cancer. Eur Radiol 29:1067–1073CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Zhang GM, Sun H, Shi B, Jin ZY, Xue HD (2017) Quantitative CT texture analysis for evaluating histologic grade of urothelial carcinoma. Abdom Radiol (NY) 42:561–568CrossRefGoogle Scholar
  35. 35.
    Davnall F, Yip CS, Ljungqvist G et al (2012) Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging 3:573–589CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Veloso Gomes F, Matos AP, Palas J et al (2015) Renal cell carcinoma subtype differentiation using single-phase corticomedullary contrast-enhanced CT. Clin Imaging 39:273–277CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Sheir KZ, El-Azab M, Mosbah A, El-Baz M, Shaaban AA (2005) Differentiation of renal cell carcinoma subtypes by multislice computerized tomography. J Urol 174:451–455 discussion 455CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Shebel HM, Elsayes KM, Sheir KZ et al (2011) Quantitative enhancement washout analysis of solid cortical renal masses using multidetector computed tomography. J Comput Assist Tomogr 35:337–342CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Ruppert-Kohlmayr AJ, Uggowitzer M, Meissnitzer T, Ruppert G (2004) Differentiation of renal clear cell carcinoma and renal papillary carcinoma using quantitative CT enhancement parameters. AJR Am J Roentgenol 183:1387–1391CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Zhang J, Lefkowitz RA, Ishill NM et al (2007) Solid renal cortical tumors: differentiation with CT. Radiology 244:494–504CrossRefGoogle Scholar
  41. 41.
    Leng S, Takahashi N, Gomez Cardona D et al (2017) Subjective and objective heterogeneity scores for differentiating small renal masses using contrast-enhanced CT. Abdom Radiol (NY) 42:1485–1492CrossRefGoogle Scholar
  42. 42.
    Yip C, Davnall F, Kozarski R et al (2015) Assessment of changes in tumor heterogeneity following neoadjuvant chemotherapy in primary esophageal cancer. Dis Esophagus 28:172–179CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Skogen K GB, Good T, Critchley G, Miles KA (2011) Imaging heterogeneity in gliomas using texture analysis. Cancer Imaging 11 Spec No A:A113Google Scholar
  44. 44.
    Castellano G, Bonilha L, Li LM, Cendes F (2004) Texture analysis of medical images. Clin Radiol 59:1061–1069CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Yang Z, Tang LH, Klimstra DS (2011) Effect of tumor heterogeneity on the assessment of Ki67 labeling index in well-differentiated neuroendocrine tumors metastatic to the liver: implications for prognostic stratification. Am J Surg Pathol 35:853–860CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Oh S, Sung DJ, Yang KS et al (2017) Correlation of CT imaging features and tumor size with Fuhrman grade of clear cell renal cell carcinoma. Acta Radiol 58:376–384CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Beddy P, Genega EM, Ngo L et al (2014) Tumor necrosis on magnetic resonance imaging correlates with aggressive histology and disease progression in clear cell renal cell carcinoma. Clin Genitourin Cancer 12:55–62CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    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 276:787–796CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Thompson RH, Kurta JM, Kaag M et al (2009) Tumor size is associated with malignant potential in renal cell carcinoma cases. J Urol 181:2033–2036CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    Turun S, Banghua L, Zheng S, Wei Q (2012) Is tumor size a reliable predictor of histopathological characteristics of renal cell carcinoma? Urol Ann 4:24–28CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    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 39:607–612CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    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 204:1013–1023CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    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–W293CrossRefPubMedPubMedCentralGoogle Scholar
  54. 54.
    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 205:1194–1202CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    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 82:342–348CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Cornejo KM, Dong F, Zhou AG et al (2015) Papillary renal cell carcinoma: correlation of tumor grade and histologic characteristics with clinical outcome. Hum Pathol 46:1411–1417CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Sika-Paotonu D, Bethwaite PB, McCredie MR, William Jordan T, Delahunt B (2006) Nucleolar grade but not Fuhrman grade is applicable to papillary renal cell carcinoma. Am J Surg Pathol 30:1091–1096CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Department of RadiologyThe First Affiliated Hospital of Guangzhou Medical UniversityGuangzhouChina
  2. 2.Department of RadiologyIndiana University School of MedicineIndianapolisUSA
  3. 3.Department of RadiologyUniversity of Florida College of MedicineJacksonvilleUSA
  4. 4.Department of Radiology, Jiangmen Central HospitalAffiliated Jiangmen Hospital of Sun YAT-SEN UniversityJiangmenChina
  5. 5.Department of OncologyHope Regional Cancer CenterPanamaUSA
  6. 6.Department of UrologyIndiana University School of MedicineIndianapolisUSA
  7. 7.Department of RadiologyMayo ClinicPhoenixUSA

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