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Differentiation between renal epithelioid angiomyolipoma and clear cell renal cell carcinoma using clear cell likelihood score

  • Kidneys, Ureters, Bladder, Retroperitoneum
  • Published:
Abdominal Radiology Aims and scope

Abstract

Purpose

Clear cell likelihood score (ccLS) may be a reliable diagnostic method for distinguishing renal epithelioid angiomyolipoma (EAML) and clear cell renal cell carcinoma (ccRCC). In this study, we aim to explore the value of ccLS in differentiating EAML from ccRCC.

Methods

We performed a retrospective analysis in which 27 EAML patients and 60 ccRCC patients underwent preoperative magnetic resonance imaging (MRI) at our institution. Two radiologists trained in the ccLS algorithm scored independently and the consistency of their interpretation was evaluated. The difference of the ccLS score was compared between EAML and ccRCC in the whole study cohort and two subgroups [small renal masses (SRM; ≤ 4 cm) and large renal masses (LRM; > 4 cm)].

Results

In total, 87 patients (59 men, 28 women; mean age, 55±11 years) with 90 renal masses (EAML: ccRCC = 1: 2) were identified. The interobserver agreement of two radiologists for the ccLS system to differentiate EAML from ccRCC was good (k = 0.71). The ccLS score in the EAML group and the ccRCC group ranged from 1 to 5 (73.3% in scores 1–2) and 2 to 5 (76.7% in scores 4–5), respectively, with statistically significant differences (P < 0.001). With the threshold value of 2, ccLS can distinguish EAML from ccRCC with the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 87.8%, 95.0%, 73.3%, 87.7%, and 88.0%, respectively. The AUC (area under the curve) was 0.913. And the distribution of the ccLS score between the two diseases was not affected by tumor size (P = 0.780).

Conclusion

The ccLS can distinguish EAML from ccRCC with high accuracy and efficiency.

Graphical Abstract

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Abbreviations

ccLS:

Clear cell likelihood score

ccRCC:

Clear cell renal cell carcinoma

EAML:

Renal epithelioid angiomyolipoma

SRM:

Small renal masses

LRM:

Large renal masses

WHO:

World health organization

MRI:

Magnetic resonance imaging

PACS:

Picture archiving and communication system

PPV:

Positive predictive value

NPV:

Negative predictive value

ROC:

Receiver operating characteristic curve

AUC:

Area under the curve

CI:

Confidence interval

DWI:

Diffusion-weighted imaging

ADER:

Arterial-delayed enhancement ratio

SEI:

Segmental enhancement inversion

References

  1. He W, Cheville JC, Sadow PM et al (2013) Epithelioid angiomyolipoma of the kidney: pathological features and clinical outcome in a series of consecutively resected tumors. Mod Pathol 26(10):1355–64. https://doi.org/10.1038/modpathol.2013.72

    Article  PubMed  Google Scholar 

  2. Bao HL, Chen X, An YX et al (2017) Clinical and pathologic analysis of 414 cases of renal angiomyolipoma in a single institution. Chin J Patho 46(6):378–82. https://doi.org/10.3760/cma.j.issn.0529-5807.2017.06.003

    Article  CAS  Google Scholar 

  3. Katabathina VS, Vikram R, Nagar AM et al (2010) Mesenchymal neoplasms of the kidney in adults: imaging spectrum with radiologic-pathologic correlation. Radiographics 30(6):1525–40. https://doi.org/10.1148/rg.306105517

    Article  PubMed  Google Scholar 

  4. Montironi, R, Cheng, L, Scarpelli, M et al (2016) Pathology and Genetics: Tumours of the Urinary System and Male Genital System: Clinical Implications of the 4th Edition of the WHO Classification and Beyond. European urology, 70(1), 120–123. https://doi.org/10.1016/j.eururo.2016.03.011

  5. Park HK, Zhang S, Wong MKK et al (2007) Clinical presentation of epithelioid angiomyolipoma. Int J Urol 14(1):21–5. https://doi.org/10.1111/j.1442-2042.2006.01665.x

    Article  PubMed  Google Scholar 

  6. Warakaulle DR, Phillips RR, Turner GDH et al (2004) Malignant monotypic epithelioid angiomyolipoma of the kidney. Clin Radiol 59(9):849–52. https://doi.org/10.1016/j.crad.2004.02.009

    Article  CAS  PubMed  Google Scholar 

  7. Tsukada J, Jinzaki M, Yao M et al (2013) Epithelioid angiomyolipoma of the kidney: radiological imaging. Int J Urol 20(11):1105–11. https://doi.org/10.1111/iju.12117

    Article  PubMed  Google Scholar 

  8. Eble JN, Amin MB, Young RH (1997) Epithelioid angiomyolipoma of the kidney: a report of five cases with a prominent and diagnostically confusing epithelioid smooth muscle component. Am J Surg Pathol 21(10):1123–30. https://doi.org/10.1097/00000478-199710000-00001

    Article  CAS  PubMed  Google Scholar 

  9. Moch H, Cubilla AL, Berney, DM et al (2022) The 2022 World Health Organization Classification of Tumours of the Urinary System and Male Genital Organs-Part A: Renal, Penile, and Testicular Tumours. European urology, 82(5), 458–468. https://doi.org/10.1016/j.eururo.2022.06.016

    Article  PubMed  Google Scholar 

  10. Froemming AT, Boland J, Cheville J et al (2013) Renal epithelioid angiomyolipoma: imaging characteristics in nine cases with radiologic-pathologic correlation and review of the literature. AJR Am J Roentgenol 200(2):W178–86. https://doi.org/10.2214/AJR.12.8776

    Article  PubMed  Google Scholar 

  11. Guo R, Kang SH, Zhong Y et al (2018) Magnetic resonance imaging findings and differential diagnosis of renal epithelioid angiomyolipoma comparing with renal no-epithelioid angiomyolipoma. Zhonghua Yi Xue Za Zhi 98(45):3701–3704. https://doi.org/10.3760/cma.j.issn.0376-2491.2018.45.014

    Article  CAS  PubMed  Google Scholar 

  12. Muglia VF, Prando A (2015) Renal cell carcinoma: histological classification and correlation with imaging findings. Radiol Bras 48(3):166–74. https://doi.org/10.1590/0100-3984.2013.1927

    Article  PubMed  PubMed Central  Google Scholar 

  13. Gnarra JR, Tory K, Weng Y et al (1994) Mutations of the VHL tumour suppressor gene in renal carcinoma. Nat Genet 7(1):85–90. https://doi.org/10.1038/ng0594-85

    Article  CAS  PubMed  Google Scholar 

  14. Sonpavde G, Willey CD, Sudarshan S (2014) Fibroblast growth factor receptors as therapeutic targets in clear-cell renal cell carcinoma. Expert Opin Investig Drugs 23(3):305–15. https://doi.org/10.1517/13543784.2014.871259

    Article  CAS  PubMed  Google Scholar 

  15. Cheville JC, Lohse CM, Zincke H et al (2004) Sarcomatoid renal cell carcinoma an examination of underlying histologic subtype and an analysis of associations with patient outcome. Am J Surg Pathol 28(4):435–41. https://doi.org/10.1097/00000478-200404000-00002

    Article  PubMed  Google Scholar 

  16. Marconi L, Dabestani S, Lam TB et al (2016) Systematic review and meta-analysis of diagnostic accuracy of percutaneous renal tumour biopsy. Eur Urol 69(4):660–73. https://doi.org/10.1016/j.eururo.2015.07.072

    Article  PubMed  Google Scholar 

  17. Lim CS, Schieda N, Silverman SG (2019) Update on indications for percutaneous renal mass biopsy in the era of advanced CT and MRI. AJR Am J Roentgenol 27:1-10. https://doi.org/10.2214/AJR.19.21093

    Article  Google Scholar 

  18. Kim TM, Ahn H, Lee HJ et al (2022) Differentiating renal epithelioid angiomyolipoma from clear cell carcinoma: using a radiomics model combined with CT imaging characteristics. Abdom Radiol (NY). 47(8):2867-2880. https://doi.org/10.1007/s00261-022-03571-9

    Article  PubMed  Google Scholar 

  19. Hötker 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-W91. https://doi.org/10.2214/AJR.16.16652

    Article  PubMed  PubMed Central  Google Scholar 

  20. Cornelis F, Tricaud E, Lasserre AS et al (2014) Routinely performed multiparametric magnetic resonance imaging helps to differentiate common subtypes of renal tumours. Eur Radiol 24(5):1068–80. https://doi.org/10.1007/s00330-014-3107-z

    Article  CAS  PubMed  Google Scholar 

  21. Allen BC, Tirman P, Jennings Clingan M et al (2014) Characterizing solid renal neoplasms with MRI in adults. Abdom Imaging 39(2):358–87. https://doi.org/10.1007/s00261-014-0074-4

    Article  PubMed  Google Scholar 

  22. Cong X, Zhang J, Xu X et al (2018) Renal epithelioid angiomyolipoma: magnetic resonance imaging characteristics. Abdom Radiol (NY) 43(10):2756–63. https://doi.org/10.1007/s00261-018-1548-6

    Article  PubMed  Google Scholar 

  23. Zhong Y, Shen Y, Pan J et al (2017) Renal epithelioid angiomyolipoma: MRI findings. Radiol Med 122(11):814–21. https://doi.org/10.1007/s11547-017-0788-9

    Article  PubMed  Google Scholar 

  24. Wang D, Gong G, Fu Y et al (2022) CT imaging findings of renal epithelioid lipid-poor angiomyolipoma. Eur Radiol 32(7):4919–4930. https://doi.org/10.1007/s00330-021-08528-y

    Article  CAS  PubMed  Google Scholar 

  25. Cui L, Zhang JG, Hu XY et al (2012) CT imaging and histopathological features of renal epithelioid angiomyolipomas. Clin Radiol 67(12):e77–82. https://doi.org/10.1016/j.crad.2012.08.006

    Article  CAS  PubMed  Google Scholar 

  26. Roussel E, Capitanio U, Kutikov A et al (2022) Novel imaging methods for renal mass characterization: a collaborative review. Eur Urol 81(5):476–88. https://doi.org/10.1016/j.eururo.2022.01.040

    Article  PubMed  PubMed Central  Google Scholar 

  27. Kay FU, Pedrosa I (2017) Imaging of solid renal masses. Radiol Clin North Am 55(2):243–58. https://doi.org/10.1016/j.rcl.2016.10.003

    Article  PubMed  Google Scholar 

  28. Canvasser NE, Kay FU, Xi Y et al (2017) Diagnostic accuracy of multiparametric magnetic resonance imaging to identify clear cell renal cell carcinoma in cT1a renal masses. J Urol 198(4):780–6. https://doi.org/10.1016/j.juro.2017.04.089

    Article  PubMed  PubMed Central  Google Scholar 

  29. Pedrosa I, Cadeddu JA (2022) How we do it: managing the indeterminate renal mass with the MRI clear cell likelihood score. Radiology 302(2):256–69. https://doi.org/10.1148/radiol.210034

    Article  PubMed  Google Scholar 

  30. Dunn M, Linehan V, Clarke SE et al (2022) Diagnostic performance and interreader agreement of the MRI clear cell likelihood score for characterization of cT1a and cT1b solid renal masses: an external validation study. AJR Am J Roentgenol 219(5):793–803. https://doi.org/10.2214/AJR.22.27378

    Article  PubMed  Google Scholar 

  31. Kay FU, Pedrosa I (2018) Imaging of solid renal masses. Urol Clin North Am. 2018;45(3):311-330. https://doi.org/10.1016/j.ucl.2018.03.013

    Article  PubMed  PubMed Central  Google Scholar 

  32. Sun MRM, Ngo L, Genega EM et al (2009) Renal cell carcinoma: dynamic contrast-enhanced MR imaging for differentiation of tumor subtypes--correlation with pathologic findings. Radiology 250(3):793–802. https://doi.org/10.1148/radiol.2503080995

    Article  PubMed  Google Scholar 

  33. Sasiwimonphan K, Takahashi N, Leibovich BC et al (2012) Small (<4 cm) renal mass: differentiation of angiomyolipoma without visible fat from renal cell carcinoma utilizing MR imaging. Radiology 263(1):160–8. https://doi.org/10.1148/radiol.12111205

    Article  PubMed  Google Scholar 

  34. Johnson BA, Kim S, Steinberg RL et al (2019) Diagnostic performance of prospectively assigned clear cell Likelihood scores (ccLS) in small renal masses at multiparametric magnetic resonance imaging. Urol Oncol 37(12):941–6. https://doi.org/10.1016/j.urolonc.2019.07.023

    Article  PubMed  PubMed Central  Google Scholar 

  35. Kuroda N, Karashima T, Inoue K et al (2015) Review of renal cell carcinoma with rhabdoid features with focus on clinical and pathobiological aspects. Pol J Pathol 66(1):3–8. https://doi.org/10.5114/pjp.2015.51147

    Article  PubMed  Google Scholar 

  36. Steinberg RL, Rasmussen RG, Johnson BA et al (2021) Prospective performance of clear cell likelihood scores (ccLS) in renal masses evaluated with multiparametric magnetic resonance imaging. Eur Radiol 31(1):314–24. https://doi.org/10.1007/s00330-020-07093-0

    Article  PubMed  Google Scholar 

  37. Brimo F, Robinson B, Guo C et al (2010) Renal epithelioid angiomyolipoma with atypia: a series of 40 cases with emphasis on clinicopathologic prognostic indicators of malignancy. Am J Surg Pathol 34(5):715–22. https://doi.org/10.1097/PAS.0b013e3181d90370

    Article  PubMed  Google Scholar 

  38. Ibrahim, A., Pelsser, V., Anidjar, M et al (2023). Performance of clear cell likelihood scores in characterizing solid renal masses at multiparametric MRI: an external validation study. Abdominal radiology, 48(3), 1033–1043. https://doi.org/10.1007/s00261-023-03799-z

    Article  PubMed  Google Scholar 

  39. Lee H, Lee JK, Kim K et al (2016) Risk of metastasis for T1a renal cell carcinoma. World J Urol 34(4):553–9. https://doi.org/10.1007/s00345-015-1659-4

    Article  CAS  PubMed  Google Scholar 

  40. Tse J R (2022) Editorial Comment: Clear Cell Likelihood Score-Another Step Toward Noninvasive Risk Stratification. AJR. American journal of roentgenology, 17:11. https://doi.org/10.2214/AJR.22.28087

    Article  Google Scholar 

  41. Mileto A, Potretzke T A (2022) Standardized Evaluation of Small Renal Masses Using the MRI Clear Cell Likelihood Score. Radiology, 303(3):600–602. https://doi.org/10.1148/radiol.220054

    Article  PubMed  Google Scholar 

  42. Rasmussen RG, Xi Y, Sibley RC et al (2022) Association of clear cell likelihood score on MRI and growth kinetics of small solid renal masses on active surveillance. AJR Am J Roentgenol 218(1):101–10. https://doi.org/10.2214/AJR.21.25979

    Article  PubMed  Google Scholar 

  43. Kay FU, Canvasser NE, Xi Y et al (2018) Diagnostic performance and interreader agreement of a standardized MR imaging approach in the prediction of small renal mass histology. Radiology 287(2):543–53. https://doi.org/10.1148/radiol.2018171557

    Article  PubMed  Google Scholar 

  44. Li XQ, Chang T (2017) The application value of support vector machine in CT differential diagnosis of renal epithelioid angiomyolipoma and clear cell renal cell carcinoma. Chinese Journal of CT and MRI 2017; 10(10):91–3. https://doi.org/10.3969/j.issn.1672-5131.2017.10.029.

    Article  Google Scholar 

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Acknowledgements

We acknowledge the financial support from the National Natural Science Foundation of China (Grant 81971580 and 82271951 and 81771785) and Beijing Municipal Natural Science Foundation (Grant 7222167).

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Author notes

  1. Yu-Wei Hao and Yun Zhang have contributed equally as first authors.

Authors and Affiliations

  1. Department of Radiology, First Medical Center, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China

    Yu-Wei Hao, Yun Zhang, Hui-Ping Guo, Wei Xu, Xu Bai, Jian Zhao, Meng-Qiu Cui, Bai-Chuan Liu, Hui-Yi Ye & Hai-Yi Wang

  2. Department of Radiology, Sixth Medical Center, Chinese PLA General Hospital, Beijing, China

    Yun Zhang

  3. Department of Pathology, First Medical Center, Chinese PLA General Hospital, Beijing, China

    Xiao-Hui Ding

  4. Department of Radiology, Linyi Central Hospital, Shandong, China

    Sheng Gao

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Hao, YW., Zhang, Y., Guo, HP. et al. Differentiation between renal epithelioid angiomyolipoma and clear cell renal cell carcinoma using clear cell likelihood score. Abdom Radiol 48, 3714–3727 (2023). https://doi.org/10.1007/s00261-023-04034-5

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