Differentiation between fat-poor angiomyolipoma and clear cell renal cell carcinoma: qualitative and quantitative analysis using arterial spin labeling MR imaging

  • Jing Ye
  • Qing XuEmail author
  • Shou-An Wang
  • Jin Zheng
  • Qing-Qiang Zhu
  • Wei-Qiang Dou
Kidneys, Ureters, Bladder, Retroperitoneum



To assess the diagnostic effectiveness of arterial spin labeling (ASL) MR imaging in differentiating fat-poor AML from clear cell renal cell carcinoma (ccRCC).


In this prospective study, 29 ccRCC patients and 9 fat-poor AML patients underwent routine anatomical MRI and ASL at 3T before surgery after signing written informed consent form. For each tumor, tumor blood flow (TBF) was measured in a region of interest (ROI) which was positioned to outline the edge of the target lesions on ASL perfusion maps. Additionally, the mean TBF values were obtained by standardizing the TBF using a blood flow measurement in the reference ROI. Moreover, a cluster containing more than 10 voxels was chosen from the renal cortex and medulla area in normal contralateral kidney as a reference ROI to calculate tumor-to-cortex ratio and tumor-to-medulla ratio. Independent sample t test was used to examine the alteration among the groups of fat-poor AML and ccRCC. ASL images were together analyzed by two radiologists to assess the following characteristics of the renal mass: predominant SI in the tumor on ASL images was lower than, as same as, or higher than SI of the cortex. For qualitative variables, Fisher’s exact test was employed to compare the proportions of these two groups. The sensitivity, specificity ,and accuracy required for discrimination of fat-poor AML from ccRCC were quantified using receiver operating characteristic (ROC) curve. The corresponding optimal cutoff value was obtained for each parameter as well.


The TBF value was significantly higher in ccRCC group than that in fat-poor AML (270.49 ± 78.88 ml/100 g/min vs. 146.68 ± 47.21 ml/100 g/min; P < 0.01). Both tumor-to-cortex and tumor-to-medulla ratios were notably higher in ccRCC group compared with those in fat-poor AML group (1.22 ± 0.26 vs. 0.74 ± 0.14, 3.13 ± 0.94 vs. 1.77 ± 0.55; P < 0.05). The values of area under the ROC curve (AUC) for TBF, tumor-to-cortex ratio, and tumor-to-medulla ratio were 0.931, 0.964, and 0.900, respectively. No significant difference in AUC values among these three measurements was observed. For qualitative variables, the SI of fat-poor AML was equal to or slightly lower than that of renal medulla and the SI of ccRCC was found to be higher than renal cortex in ASL.


ASL MRI performs well in differentiating fat-poor AML from ccRCC in both qualitative and quantitative analyses.


Fat-poor angiomyolipoma Clear cell renal cell carcinoma Magnetic resonance imaging Arterial spin labeling 


Author contributions

Ye J is the guarantor of integrity of the entire study; all authors contributed to the approval of final version of submitted manuscript and agree to ensure that any questions related to the work are appropriately resolved; Zheng J contributed to clinical case studies; Wang SA contributed to statistical analysis. All authors contributed to manuscript editing.


National Natural Science Foundation of China, 81401384. Social Develop Foundation of Yangzhou, 2017066. Yangzhou City Science and Education Strengthening Leading Talents Project, LJRC201810. Yangzhou City Science and Education Strengthening Key Talents Project, ZDRC201873. Jiangsu Province “Six First Project” for High-Level Health Professionals, LGY2019032.

Compliance with ethical standards

Conflicts of interest

All authors declare that they have no any conflict of interest.

Informed consent

Informed consent was obtained from the patient included in the study.


  1. 1.
    ME Snyder, A Bach, MW Kattan, GV Raj, VE Reuter, P Russo. Incidence of benign lesions for clinically localized renal masses smaller than 7 cm in radiological diameter: influence of sex. The Journal of urology. 2006;176(null):2391-5; discussion 5-6. CrossRefGoogle Scholar
  2. 2.
    Schachter LR, Cookson MS, Chang SS, et al. Second prize: frequency of benign renal cortical tumors and histologic subtypes based on size in a contemporary series: what to tell our patients. Journal of Endourology. 2007;21(8):819. CrossRefPubMedGoogle Scholar
  3. 3.
    Y Fujii, Y Komai, K Saito, et al. Incidence of benign pathologic lesions at partial nephrectomy for presumed RCC renal masses: Japanese dual-center experience with 176 consecutive patients. Urology. 2008;72(3):598-602. CrossRefGoogle Scholar
  4. 4.
    Richard PO, Jewett MAS, Bhatt JR, et al. Renal Tumor Biopsy for Small Renal Masses: A Single-center 13-year Experience. European urology. 2015;68(6):1007-13. CrossRefPubMedGoogle Scholar
  5. 5.
    Remzi M, Özsoy M, Klingler HC, et al. Are Small Renal Tumors Harmless? Analysis of Histopathological Features According to Tumors 4 Cm or Less in Diameter. Journal of Urology. 2006;176(3):896-9. CrossRefPubMedGoogle Scholar
  6. 6.
    Pahernik S, Ziegler S, Roos F, Melchior SW, Thüroff JW. Small renal tumors: correlation of clinical and pathological features with tumor size. J Urol. 2007;178(2):414-7. CrossRefPubMedGoogle Scholar
  7. 7.
    K Sasiwimonphan, N Takahashi, BC Leibovich, RE Carter, TD Atwell, A Kawashima. Small (< 4 cm) renal mass: differentiation of angiomyolipoma without visible fat from renal cell carcinoma utilizing MR imaging. Radiology. 2012;263(1):160-8. CrossRefGoogle Scholar
  8. 8.
    Katabathina VS, Raghunandan V, Nagar AM, Pheroze T, Menias CO, Prasad SR. Mesenchymal neoplasms of the kidney in adults: imaging spectrum with radiologic-pathologic correlation. Radiographics A Review Publication of the Radiological Society of North America Inc. 2010;30(6):1525-40. CrossRefGoogle Scholar
  9. 9.
    Schieda N, Avruch L, Flood TA. Small (< 1 cm) incidental echogenic renal cortical nodules: chemical shift MRI outperforms CT for confirmatory diagnosis of angiomyolipoma (AML). Insights Into Imaging. 2014;5(3):295-9. CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Burdeny DA, Semelka RC, Kelekis NL, Reinhold C,., Ascher SM. Small (< 1.5 cm) angiomyolipomas of the kidney: characterization by the combined use of in-phase and fat-attenuated MR techniques. Magnetic Resonance Imaging. 1997;15(2):141-5. CrossRefGoogle Scholar
  11. 11.
    Sherman JL, Hartman DS, Friedman AC, Madewell JE, Davis CJ, Goldman SM. Angiomyolipoma: computed tomographic-pathologic correlation of 17 cases. AJR American journal of roentgenology. 1981;137(6):1221-6. CrossRefPubMedGoogle Scholar
  12. 12.
    L Richmond, M Atri, C Sherman, S Sharir. Renal cell carcinoma containing macroscopic fat on CT mimics an angiomyolipoma due to bone metaplasia without macroscopic calcification. The British journal of radiology. 2010;83(992):e179-81. CrossRefGoogle Scholar
  13. 13.
    Israel GM, Nicole H, Elizabeth H, Glenn K. The use of opposed-phase chemical shift MRI in the diagnosis of renal angiomyolipomas. AJR American journal of roentgenology. 2005;184(6):1868-72. CrossRefPubMedGoogle Scholar
  14. 14.
    Zhang YY, Luo S, Liu Y, Xu RT. Angiomyolipoma with minimal fat: Differentiation from papillary renal cell carcinoma by helical CT. Clinical Radiology. 2013;68(4):365-70. CrossRefPubMedGoogle Scholar
  15. 15.
    Sant GR, Heaney JA, Ucci AA, Sarno RC, Meares EM. Computed tomographic findings in renal angiomyolipoma: an histologic correlation. Urology. 1984;24(3):293-6. CrossRefPubMedGoogle Scholar
  16. 16.
    Jeong Kon K, Soo-Youn P, Jeong-Hee S, Kyoung-Sik C. Angiomyolipoma with minimal fat: differentiation from renal cell carcinoma at biphasic helical CT. Radiology. 2004;230(3):677-84. CrossRefGoogle Scholar
  17. 17.
    Claus S, Herts BR, Motta-Ramirez GA, et al. Angiomyolipoma with minimal fat on MDCT: can counts of negative-attenuation pixels aid diagnosis? AJR American journal of roentgenology. 2009;192(2):438-43. CrossRefGoogle Scholar
  18. 18.
    Yeon KJ, Jeong Kon K, Namkug K, Kyoung-Sik C. CT histogram analysis: differentiation of angiomyolipoma without visible fat from renal cell carcinoma at CT imaging. Radiology. 2008;246(2):472-9. CrossRefGoogle Scholar
  19. 19.
    M Jinzaki, A Tanimoto, Y Narimatsu, et al. Angiomyolipoma: imaging findings in lesions with minimal fat. Radiology. 1997;205(2):497-502. CrossRefGoogle Scholar
  20. 20.
    Jeong Kon K, Soo Hyun K, Yoon Jin J, et al. Renal angiomyolipoma with minimal fat: differentiation from other neoplasms at double-echo chemical shift FLASH MR imaging. Radiology. 2006;239(1):174-80. CrossRefGoogle Scholar
  21. 21.
    Nicole H, Long N, Genega EM, et al. Angiomyolipoma with minimal fat: can it be differentiated from clear cell renal cell carcinoma by using standard MR techniques? Radiology. 2012;265(2):468-77. CrossRefGoogle Scholar
  22. 22.
    Mesut R, Mehmet O, Hans-Christoph K, et al. Are small renal tumors harmless? Analysis of histopathological features according to tumors 4 cm or less in diameter. Journal of Urology. 2006;176(3):896-9. CrossRefGoogle Scholar
  23. 23.
    Roberts DA, Detre JA, Bolinger L,., et al. Renal perfusion in humans: MR imaging with spin tagging of arterial water. Radiology. 1995;196(1):281-6. CrossRefPubMedGoogle Scholar
  24. 24.
    Alsop DC, Detre JA. Multisection cerebral blood flow MR imaging with continuous arterial spin labeling. Radiology. 1998;208(2):410-6. CrossRefPubMedGoogle Scholar
  25. 25.
    Lanzman RS, Wittsack HJ, Martirosian P, et al. Quantification of renal allograft perfusion using arterial spin labeling MRI: initial results. European Radiology. 2010;20(6):1485-91. CrossRefPubMedGoogle Scholar
  26. 26.
    Michael F, Petros M, Juergen L, et al. Perfusion MR imaging with FAIR true FISP spin labeling in patients with and without renal artery stenosis: initial experience. Radiology. 2006;238(3):1013-21. CrossRefGoogle Scholar
  27. 27.
    L Maccotta, JA Detre, DC Alsop. The efficiency of adiabatic inversion for perfusion imaging by arterial spin labeling. NMR in biomedicine. 1997;10(null):216-21.;
  28. 28.
    Lanzman RS, Robson PM, Sun MR, et al. Arterial spin-labeling MR imaging of renal masses: correlation with histopathologic findings. Radiology. 2012;265(3):799. CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    BR Lane, H Aydin, TL Danforth, et al. Clinical correlates of renal angiomyolipoma subtypes in 209 patients: classic, fat poor, tuberous sclerosis associated and epithelioid. The Journal of urology. 2008;180(3):836-43. CrossRefGoogle Scholar
  30. 30.
    Jason H, Fogarty JD, Hoenig DM, Maomi L, Robert B, Reza G. Imaging characteristics of minimal fat renal angiomyolipoma with histologic correlations. Urology. 2005;66(6):1155-9. CrossRefGoogle Scholar
  31. 31.
    Milner J, Mcneil BJ, Proud K, et al. Fat poor renal angiomyolipoma: patient, computerized tomography and histological findings. Journal of Urology. 2006;176(3):905-9. CrossRefPubMedGoogle Scholar
  32. 32.
    Petros M, Uwe K, Irina M, Fritz S. FAIR true-FISP perfusion imaging of the kidneys. Magnetic Resonance in Medicine. 2004;51(2):353–61. CrossRefGoogle Scholar
  33. 33.
    RL Wolf, J Wang, S Wang, et al. Grading of CNS neoplasms using continuous arterial spin labeled perfusion MR imaging at 3 Tesla. Journal of magnetic resonance imaging : JMRI. 2005;22(4):475-82. CrossRefGoogle Scholar
  34. 34.
    Zhang Y, Kapur P, Yuan Q, et al. Tumor Vascularity in Renal Masses: Correlation ofArterial Spin-Labeled and Dynamic Contrast-Enhanced Magnetic Resonance Imaging Assessments. Clinical Genitourinary Cancer. 2016;14(1):e25-e36. CrossRefPubMedGoogle Scholar
  35. 35.
    Ching BC, Tan HS, Tan PH, et al. Differential radiologic characteristics of renal tumours on multiphasic computed tomography. Singapore Medical Journal. 2016;58(5). CrossRefGoogle Scholar
  36. 36.
    Mi-Hyun K, Jungbok L, Gyunggoo C, Kyoung-Sik C, Jungmi K, Jeong Kon K. MDCT-based scoring system for differentiating angiomyolipoma with minimal fat from renal cell carcinoma. Acta Radiologica. 2013;54(10):1201-9. CrossRefGoogle Scholar
  37. 37.
    Ding Y, Zeng M, Rao S, Chen C, Fu C, Zhou J. Comparison of Biexponential and Monoexponential Model of Diffusion-Weighted Imaging for Distinguishing between Common Renal Cell Carcinoma and Fat Poor Angiomyolipoma. Korean Journal of Radiology. 2016;17(6):853-63. CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Feng Z, Rong P, Cao P, et al. Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma. European Radiology. 2018;28(4):1625-33. CrossRefPubMedGoogle Scholar
  39. 39.
    Wintermark M, Sesay M, Barbier E, et al. Comparative overview of brain perfusion imaging techniques. Journal of Neuroradiology. 2005;32(5):294-314. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Medical Imaging, Clinic Medical School, Northern Jiangsu Province HospitalYangzhou UniversityYangzhouChina
  2. 2.MR Research, GE HealthcareBeijingChina

Personalised recommendations