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European Radiology

, Volume 30, Issue 1, pp 1–10 | Cite as

Papillary vs clear cell renal cell carcinoma. Differentiation and grading by iodine concentration using DECT—correlation with microvascular density

  • Julian MarconEmail author
  • Anno Graser
  • David Horst
  • Jozefina Casuscelli
  • Annabel Spek
  • Christian G. Stief
  • Maximilian F. Reiser
  • Johannes Rübenthaler
  • Alexander Buchner
  • Michael Staehler
Urogenital
  • 124 Downloads

Abstract

Objectives

Various imaging methods have been evaluated regarding non-invasive differentiation of renal cell carcinoma (RCC) subtypes. Dual-energy computed tomography (DECT) allows iodine concentration (IC) analysis as a correlate of tissue perfusion. Microvascular density (MVD) in histopathology specimens is evaluated to determine intratumoral vascularization. The objective of this study was to assess the potential of IC and MVD regarding the differentiation between papillary and clear cell RCC and between well- and dedifferentiated tumors. Further, we aimed to investigate a possible correlation between these parameters.

Methods

DECT imaging series of 53 patients with clear cell RCC (ccRCC) and 15 with papillary RCC (pRCC) were analyzed regarding IC. Histology samples were stained using CD31/CD34 monoclonal antibodies; MVD was evaluated digitally. Statistical analysis included performance of Mann-Whitney U test, ROC analysis, and Spearman rank correlation.

Results

Analysis of IC demonstrated significant differences between ccRCC and pRCC (p < 0.001). A cutoff value of ≤ 3.1 mg/ml at IC analysis allowed identification of pRCC with an accuracy of 86.8%. Within the ccRCC subgroup, G1/G2 tumors could significantly be differentiated from G3/G4 carcinomas (p = 0.045). A significant positive correlation between IC and MVD could be determined for the entire RCC cohort and the ccRCC subgroup. Limitations include the small percentage of pRCCs.

Conclusions

IC analysis is a useful method to differentiate pRCC from ccRCC. The significant positive correlation between IC and MVD indicates valid representation of tumor perfusion by DECT.

Key Points

• Analysis of iodine concentration using DECT imaging could reliably distinguish papillary from clear cell subtypes of renal cell cancer (RCC).

• A cutoff value of 3.1 mg/ml allowed a distinction between papillary and clear cell RCCs with an accuracy of 86.8%.

• The positive correlation with microvascular density in tumor specimens indicates correct display of perfusion by iodine concentration analysis.

Keywords

Carcinoma, renal cell Kidney neoplasms Neovascularization, pathologic Cell differentiation Tomography, X-ray computed 

Abbreviations

AUC

Area under the curve

ccRCC

Clear cell renal cell carcinoma

CD

Cluster of differentiation

chRCC

Chromophobe renal cell carcinoma

CT

Computed tomography

DECT

Dual-energy computed tomography

FOV

Field of view

H&E

Hematoxylin and eosin

HU

Hounsfield unit

kVp

Kilovoltage peak

MRI

Magnetic resonance imaging

mTOR

Mammalian target of rapamycin

MVD

Microvascular density

pRCC

Papillary renal cell carcinoma

RCC

Renal cell carcinoma

ROC

Receiver operating characteristic

VEGF

Vascular endothelial growth factor

VNC

Virtual non-contrast

VNE

Virtual non-enhanced

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 Michael Staehler.

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 obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Experimental

• Performed at one institution

Supplementary material

330_2019_6298_MOESM1_ESM.docx (15 mb)
ESM 1 (DOCX 15.0 mb)

Reference list

  1. 1.
    Ljungberg B, Campbell SC, Choi HY et al (2011) The epidemiology of renal cell carcinoma. Eur Urol 60(4):615–621PubMedGoogle Scholar
  2. 2.
    Muglia VF, Prando A (2015) Renal cell carcinoma: histological classification and correlation with imaging findings. Radiol Bras 48(3):166–174PubMedPubMedCentralGoogle Scholar
  3. 3.
    Capitanio U, Cloutier V, Zini L et al (2009) A critical assessment of the prognostic value of clear cell, papillary and chromophobe histological subtypes in renal cell carcinoma: a population-based study. BJU Int 103(11):1496–1500PubMedGoogle Scholar
  4. 4.
    Ljungberg B, Bensalah K, Canfield S et al (2015) EAU guidelines on renal cell carcinoma: 2014 update. Eur Urol 67(5):913–924PubMedGoogle Scholar
  5. 5.
    Novara G, Martignoni G, Artibani W, Ficarra V (2007) Grading systems in renal cell carcinoma. J Urol 177(2):430–436PubMedGoogle Scholar
  6. 6.
    Nico B, Benagiano V, Mangieri D, Maruotti N, Vacca A, Ribatti D (2008) Evaluation of microvascular density in tumors: pro and contra. Histol Histopathol 23(5):601–607Google Scholar
  7. 7.
    Meert AP, Paesmans M, Martin B et al (2002) The role of microvessel density on the survival of patients with lung cancer: a systematic review of the literature with meta-analysis. Br J Cancer 87(7):694–701PubMedPubMedCentralGoogle Scholar
  8. 8.
    Vikram R, Ng CS, Tamboli P et al (2009) Papillary renal cell carcinoma: radiologic-pathologic correlation and spectrum of disease. Radiographics. 29(3):741–754 discussion 755–747PubMedGoogle 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(2):444–453PubMedGoogle Scholar
  10. 10.
    Hotker AM, Mazaheri Y, Wibmer A et al (2017) Differentiation of clear cell renal cell carcinoma from other renal cortical tumors by use of a quantitative multiparametric MRI approach. AJR Am J Roentgenol 208(3):W85–W91PubMedPubMedCentralGoogle Scholar
  11. 11.
    Graser A, Johnson TR, Chandarana H, Macari M (2009) Dual energy CT: preliminary observations and potential clinical applications in the abdomen. Eur Radiol 19(1):13–23PubMedGoogle Scholar
  12. 12.
    Graser A, Becker CR, Staehler M et al (2010) Single-phase dual-energy CT allows for characterization of renal masses as benign or malignant. Invest Radiol 45(7):399–405PubMedGoogle Scholar
  13. 13.
    Weidner N, Semple JP, Welch WR, Folkman J (1991) Tumor angiogenesis and metastasis--correlation in invasive breast carcinoma. N Engl J Med 324(1):1–8PubMedGoogle Scholar
  14. 14.
    Weidner N (1995) Intratumor microvessel density as a prognostic factor in cancer. Am J Pathol 147(1):9–19PubMedPubMedCentralGoogle Scholar
  15. 15.
    Kroeger N, Choueiri TK, Lee JL et al (2014) Survival outcome and treatment response of patients with late relapse from renal cell carcinoma in the era of targeted therapy. Eur Urol 65(6):1086–1092PubMedGoogle Scholar
  16. 16.
    Ascenti G, Mileto A, Krauss B et al (2013) Distinguishing enhancing from nonenhancing renal masses with dual-source dual-energy CT: iodine quantification versus standard enhancement measurements. Eur Radiol 23(8):2288–2295PubMedGoogle Scholar
  17. 17.
    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(1):96–105PubMedGoogle Scholar
  18. 18.
    Mileto A, Marin D, Alfaro-Cordoba M et al (2014) Iodine quantification to distinguish clear cell from papillary renal cell carcinoma at dual-energy multidetector CT: a multireader diagnostic performance study. Radiology. 273(3):813–820PubMedGoogle Scholar
  19. 19.
    Hsieh JJ, Purdue MP, Signoretti S et al (2017) Renal cell carcinoma. Nat Rev Dis Primers 3:17009PubMedPubMedCentralGoogle Scholar
  20. 20.
    Apfaltrer P, Meyer M, Meier C et al (2012) Contrast-enhanced dual-energy CT of gastrointestinal stromal tumors: is iodine-related attenuation a potential indicator of tumor response? Invest Radiol 47(1):65–70PubMedGoogle Scholar
  21. 21.
    Hellbach K, Sterzik A, Sommer W et al (2016) Dual energy CT allows for improved characterization of response to antiangiogenic treatment in patients with metastatic renal cell cancer. Eur Radiol 27:2532–2537PubMedGoogle Scholar
  22. 22.
    Cheng SH, Liu JM, Liu QY et al (2014) Prognostic role of microvessel density in patients with renal cell carcinoma: a meta-analysis. Int J Clin Exp Pathol 7(9):5855–5863PubMedPubMedCentralGoogle Scholar
  23. 23.
    Zocchi MR, Poggi A (2004) PECAM-1, apoptosis and CD34+ precursors. Leuk Lymphoma 45(11):2205–2213PubMedGoogle Scholar
  24. 24.
    Jinzaki M, Tanimoto A, Mukai M et al (2000) Double-phase helical CT of small renal parenchymal neoplasms: correlation with pathologic findings and tumor angiogenesis. J Comput Assist Tomogr 24(6):835–842PubMedGoogle Scholar
  25. 25.
    Jiang Y, Li J, Wang J et al (2016) Assessment of vascularity in hepatic alveolar echinococcosis: comparison of quantified dual-energy CT with histopathologic parameters. PLoS One 11(2):e0149440PubMedPubMedCentralGoogle Scholar
  26. 26.
    Young JR, Coy H, Douek M et al (2017) Type 1 papillary renal cell carcinoma: differentiation from type 2 papillary RCC on multiphasic MDCT. Abdom Radiol (NY) 42(7):1911–1918Google Scholar
  27. 27.
    Graser A, Johnson TR, Hecht EM et al (2009) Dual-energy CT in patients suspected of having renal masses: can virtual nonenhanced images replace true nonenhanced images? Radiology. 252(2):433–440PubMedGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • Julian Marcon
    • 1
    Email author
  • Anno Graser
    • 2
  • David Horst
    • 3
  • Jozefina Casuscelli
    • 1
  • Annabel Spek
    • 1
  • Christian G. Stief
    • 1
  • Maximilian F. Reiser
    • 2
  • Johannes Rübenthaler
    • 2
  • Alexander Buchner
    • 1
  • Michael Staehler
    • 1
  1. 1.Department of UrologyLudwig-Maximilians-Universität MünchenMunichGermany
  2. 2.Department of RadiologyLudwig-Maximilians-Universität MünchenMunichGermany
  3. 3.Institute of PathologyLudwig-Maximilians-Universität MünchenMunichGermany

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