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European Radiology

, Volume 26, Issue 7, pp 2242–2251 | Cite as

MRI evaluation of small (<4cm) solid renal masses: multivariate modeling improves diagnostic accuracy for angiomyolipoma without visible fat compared to univariate analysis

  • Nicola Schieda
  • Marc Dilauro
  • Bardia Moosavi
  • Taryn Hodgdon
  • Gregory O. Cron
  • Matthew D. F. McInnes
  • Trevor A. Flood
Urogenital

Abstract

Objective

To assess MRI for diagnosis of angiomyolipoma without visible fat (AMLwvf).

Material and methods

With IRB approval, a retrospective study in consecutive patients with contrast-enhanced (CE)-MRI and <4 cm solid renal masses from 2002–2013 was performed. Ten AMLwvf were compared to 77 RCC; 33 clear cell (cc), 35 papillary (p), 9 chromophobe (ch). A blinded radiologist measured T2W signal-intensity ratio (SIR), chemical-shift (CS) SI-index and area under CE-MRI curve (CE-AUC). Regression modeling and ROC analysis was performed.

Results

T2W-SIR was lower in AMLwvf (0.64 ± 0.12) compared to cc-RCC (1.37 ± 0.30, p < 0.001), ch-RCC (0.94 ± 0.19, p = 0.005) but not p-RCC (0.74 ± 0.17, p = 0.2). CS-SI index was higher in AMLwvf (16.1 ± 31.5 %) compared to p-RCC (-5.2 ± 26.1 %, p = 0.02) but not ch-RCC (3.0 ± 12.5 %, p = 0.1) or cc-RCC (7.7 ± 17.9 %,p = 0.1). CE-AUC was higher in AMLwvf (515.7 ± 144.7) compared to p-RCC (154.5 ± 92.8, p < 0.001) but not ch-RCC (341.5 ± 202.7, p = 0.07) or cc-RCC (520.9 ± 276.9, p = 0.95). Univariate ROC-AUC were: T2SIR = 0.86 (CI 0.77–0.96); CE-AUC = 0.76 (CI 0.65–0.87); CS-SI index = 0.66 (CI 0.4.3–0.85). Logistic regression models improved ROC-AUC, A) T2 SIR + CE-AUC = 0.97 (CI 0.93–1.0) and T2 SIR + CS-SI index = 0.92 (CI 0.84–0.99) compared to univariate analyses (p < 0.05). The optimal sensitivity/specificity of T2SIR + CE-AUC and T2SIR + CS-SI index were 100/88.8 % and 60/97.4 %.

Conclusion

MRI, using multi-variate modelling, is accurate for diagnosis of AMLwvf.

Key Points

AMLwvf are difficult to prospectively diagnose with imaging.

MRI findings associated with AMLwvf overlap with various RCC subtypes.

T2W-SI combined with chemical-shift SI-index is specific for AMLwvf but lacks sensitivity.

T2W-SI combined with AUC CE-MRI is sensitive and specific for AMLwvf.

Models incorporating two or more findings are more accurate than univariate analysis.

Keywords

Angiomyolipoma Magnetic resonance imaging Minimal fat T2 weighted imaging Contrast enhanced 

Notes

Acknowledgements

The scientific guarantor of this publication is Nicola Schieda, MD FRCPC. 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. The authors state that this work has not received any funding. One of the authors has significant statistical expertise; Gregory O Cron, PhD (The Ottawa Hospital Research Institute/The University of Ottawa). Institutional Review Board approval was obtained. Written informed consent was waived by the Institutional Review Board. Some study subjects or cohorts have been previously reported.

The AML without visible fat cohort was previously studied and published using non-contrast enhanced and contrast-enhanced CT. Six of ten AML without visible fat were previously studied with MRI to show radiologic-pathologic correlation of independent MR imaging findings on T2W and chemical-shift imaging (European Radiology 2015). The current study evaluates the diagnostic accuracy of MRI findings evaluated independently and in regression models compared to RCC, which was previously never performed in this cohort of patients.

Schieda N, Hodgdon T, El-Khodary M, Flood TA, McInnes MD (2014) Unenhanced CT for the Diagnosis of Minimal-Fat Renal Angiomyolipoma. AJR Am J Roentgenol 203:1236-1241

Hodgdon T MM, Schieda N, Lamb L, Flood TA, Thornhill R. (2015) Quantitative CT texture analysis:

Can it differentiate between minimal fat renal angiomyolipoma (mfAML) and renal cell carcinoma on non-contrast enhanced computed tomography (NECT)? Radiology. The full citation is now available: http://www.ncbi.nlm.nih.gov/pubmed/25906183.

Methodology: retrospective, case-control study, performed at one institution.

Conflict of interest

The author(s) declare that they have no conflict of interests.

References

  1. 1.
    Eble JNSG, Epstein JI, Sesterhenn IA (2004) World Health Organization classification of tumors: pathology and genetics of tumors of the urinary system and male genital organs. Lyon, Fr. Available via http://www.iarc.fr/en/publications/pdfs-online/pat-gen/bb7/BB7.pdf2013
  2. 2.
    Bissler JJ, Kingswood JC (2004) Renal angiomyolipomata. Kidney Int 66:924–934CrossRefPubMedGoogle Scholar
  3. 3.
    Schieda N, Avruch L, Flood TA (2014) Small (<1 cm) incidental echogenic renal cortical nodules: chemical shift MRI outperforms CT for confirmatory diagnosis of angiomyolipoma (AML). Insights Imaging 5:295–299CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Schieda N, Hodgdon T, El-Khodary M, Flood TA, McInnes MD (2014) Unenhanced CT for the diagnosis of minimal-fat renal angiomyolipoma. AJR Am J Roentgenol 203:1236–1241CrossRefPubMedGoogle Scholar
  5. 5.
    Schieda N, Kielar AZ, Al Dandan O, McInnes MD, Flood TA (2014) Ten uncommon and unusual variants of renal angiomyolipoma (AML): radiologic-pathologic correlation. Clin RadiolGoogle Scholar
  6. 6.
    Schieda N, Ramchandani P, Siegelman ES (2013) Computed tomographic findings of radiation-induced acute adrenal injury with associated radiation nephropathy: a case report. Acta Radiol Short Rep 2:2047981613501305PubMedPubMedCentralGoogle Scholar
  7. 7.
    Jinzaki M, Silverman SG, Akita H, Nagashima Y, Mikami S, Oya M (2014) Renal angiomyolipoma: a radiological classification and update on recent developments in diagnosis and management. Abdom ImagingGoogle Scholar
  8. 8.
    Remzi M, Ozsoy M, Klingler HC et al (2006) Are small renal tumors harmless? Analysis of histopathological features according to tumors 4 cm or less in diameter. J Urol 176:896–899CrossRefPubMedGoogle Scholar
  9. 9.
    Violette P, Abourbih S, Szymanski KM et al (2012) Solitary solid renal mass: can we predict malignancy? BJU Int 110:E548–E552CrossRefPubMedGoogle Scholar
  10. 10.
    Simpfendorfer C, Herts BR, Motta-Ramirez GA et al (2009) Angiomyolipoma with minimal fat on MDCT: can counts of negative-attenuation pixels aid diagnosis? AJR Am J Roentgenol 192:438–443CrossRefPubMedGoogle Scholar
  11. 11.
    Kim JK, Kim SH, Jang YJ et al (2006) Renal angiomyolipoma with minimal fat: differentiation from other neoplasms at double-echo chemical shift FLASH MR imaging. Radiology 239:174–180CrossRefPubMedGoogle Scholar
  12. 12.
    Kim JK, Park SY, Shon JH, Cho KS (2004) Angiomyolipoma with minimal fat: differentiation from renal cell carcinoma at biphasic helical CT. Radiology 230:677–684CrossRefPubMedGoogle Scholar
  13. 13.
    Kim JY, Kim JK, Kim N, Cho KS (2008) CT histogram analysis: differentiation of angiomyolipoma without visible fat from renal cell carcinoma at CT imaging. Radiology 246:472–479CrossRefPubMedGoogle Scholar
  14. 14.
    Hindman N, Ngo L, Genega EM et al (2012) Angiomyolipoma with minimal fat: can it be differentiated from clear cell renal cell carcinoma by using standard MR techniques? Radiology 265:468–477CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Zhang YY, Luo S, Liu Y, Xu RT (2013) Angiomyolipoma with minimal fat: differentiation from papillary renal cell carcinoma by helical CT. Clin Radiol 68:365–370CrossRefPubMedGoogle Scholar
  16. 16.
    Chaudhry HS, Davenport MS, Nieman CM, Ho LM, Neville AM (2012) Histogram analysis of small solid renal masses: differentiating minimal fat angiomyolipoma from renal cell carcinoma. AJR Am J Roentgenol 198:377–383CrossRefPubMedGoogle Scholar
  17. 17.
    Jinzaki M, Tanimoto A, Narimatsu Y et al (1997) Angiomyolipoma: imaging findings in lesions with minimal fat. Radiology 205:497–502CrossRefPubMedGoogle Scholar
  18. 18.
    Sasiwimonphan K, Takahashi N, Leibovich BC, Carter RE, Atwell TD, Kawashima A (2012) Small (<4 cm) renal mass: differentiation of angiomyolipoma without visible fat from renal cell carcinoma utilizing MR imaging. Radiology 263:160–168CrossRefPubMedGoogle Scholar
  19. 19.
    Ferre R, Cornelis F, Verkarre V et al (2014) Double-echo gradient chemical shift MR imaging fails to differentiate minimal fat renal angiomyolipomas from other homogeneous solid renal tumors. Eur J RadiolGoogle Scholar
  20. 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 24:1068–1080CrossRefPubMedGoogle Scholar
  21. 21.
    Choi HJ, Kim JK, Ahn H, Kim CS, Kim MH, Cho KS (2011) Value of T2-weighted MR imaging in differentiating low-fat renal angiomyolipomas from other renal tumors. Acta Radiol 52:349–353CrossRefPubMedGoogle Scholar
  22. 22.
    Hakim SW, Schieda N, Hodgdon T, McInnes MD, Dilauro M, Flood TA (2015) Angiomyolipoma (AML) without visible fat: Ultrasound, CT and MR imaging features with pathological correlation. Eur RadiolGoogle Scholar
  23. 23.
    Prasad SR, Humphrey PA, Catena JR et al (2006) Common and uncommon histologic subtypes of renal cell carcinoma: imaging spectrum with pathologic correlation. Radiographics 26:1795–1806, discussion 1806-1710CrossRefPubMedGoogle Scholar
  24. 24.
    Campbell N, Rosenkrantz AB, Pedrosa I (2014) MRI phenotype in renal cancer: is it clinically relevant? Top Magn Reson Imaging 23:95–115CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Ramamurthy NK, Moosavi B, McInnes MD, Flood TA, Schieda N (2014) Multiparametric MRI of solid renal masses: pearls and pitfalls. Clin RadiolGoogle Scholar
  26. 26.
    Pedrosa I, Sun MR, Spencer M et al (2008) MR imaging of renal masses: correlation with findings at surgery and pathologic analysis. Radiographics 28:985–1003CrossRefPubMedGoogle Scholar
  27. 27.
    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 12:55–62CrossRefPubMedGoogle Scholar
  28. 28.
    Karlo CA, Donati OF, Burger IA et al (2013) MR imaging of renal cortical tumours: qualitative and quantitative chemical shift imaging parameters. Eur Radiol 23:1738–1744CrossRefPubMedGoogle Scholar
  29. 29.
    Schieda N, van der Pol CB, Moosavi B, McInnes MD, Mai KT, Flood TA (2015) Intracellular lipid in papillary renal cell carcinoma (pRCC): T2 weighted (T2W) MRI and pathologic correlation. Eur RadiolGoogle Scholar
  30. 30.
    Yamashita Y, Takahashi M, Watanabe O et al (1992) Small renal cell carcinoma: pathologic and radiologic correlation. Radiology 184:493–498CrossRefPubMedGoogle Scholar
  31. 31.
    Frank I, Blute ML, Cheville JC, Lohse CM, Weaver AL, Zincke H (2003) Solid renal tumors: an analysis of pathological features related to tumor size. J Urol 170:2217–2220CrossRefPubMedGoogle Scholar
  32. 32.
    Delahunt B, Eble JN (1997) Papillary renal cell carcinoma: a clinicopathologic and immunohistochemical study of 105 tumors. Mod Pathol 10:537–544PubMedGoogle Scholar
  33. 33.
    Lefevre M, Couturier J, Sibony M et al (2005) Adult papillary renal tumor with oncocytic cells: clinicopathologic, immunohistochemical, and cytogenetic features of 10 cases. Am J Surg Pathol 29:1576–1581CrossRefPubMedGoogle Scholar
  34. 34.
    Renshaw AA, Corless CL (1995) Papillary renal cell carcinoma. Histology and immunohistochemistry. Am J Surg Pathol 19:842–849CrossRefPubMedGoogle Scholar
  35. 35.
    Thoenes W, Storkel S, Rumpelt HJ, Moll R, Baum HP, Werner S (1988) Chromophobe cell renal carcinoma and its variants--a report on 32 cases. J Pathol 155:277–287CrossRefPubMedGoogle Scholar
  36. 36.
    Ferl GZ, Xu L, Friesenhahn M, Bernstein LJ, Barboriak DP, Port RE (2010) An automated method for nonparametric kinetic analysis of clinical DCE-MRI data: application to glioblastoma treated with bevacizumab. Magn Reson Med 63:1366–1375CrossRefPubMedGoogle Scholar
  37. 37.
    Walker-Samuel S, Leach MO, Collins DJ (2006) Evaluation of response to treatment using DCE-MRI: the relationship between initial area under the gadolinium curve (IAUGC) and quantitative pharmacokinetic analysis. Phys Med Biol 51:3593–3602CrossRefPubMedGoogle Scholar
  38. 38.
    Chung MS, Choi HJ, Kim MH, Cho KS (2014) Comparison of t2-weighted MRI with and without fat suppression for differentiating renal angiomyolipomas without visible fat from other renal tumors. AJR Am J Roentgenol 202:765–771CrossRefPubMedGoogle Scholar
  39. 39.
    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 178:1499–1506CrossRefPubMedGoogle Scholar
  40. 40.
    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 183:1387–1391CrossRefPubMedGoogle Scholar
  41. 41.
    Vargas HA, Chaim J, Lefkowitz RA et al (2012) Renal cortical tumors: use of multiphasic contrast-enhanced MR imaging to differentiate benign and malignant histologic subtypes. Radiology 264:779–788CrossRefPubMedGoogle Scholar
  42. 42.
    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 267:444–453CrossRefPubMedGoogle Scholar
  43. 43.
    Lee-Felker SA, Felker ER, Tan N et al (2014) Qualitative and quantitative MDCT features for differentiating clear cell renal cell carcinoma from other solid renal cortical masses. AJR Am J Roentgenol 203:W516–W524CrossRefPubMedGoogle Scholar
  44. 44.
    Yang CW, Shen SH, Chang YH et al (2013) Are there useful CT features to differentiate renal cell carcinoma from lipid-poor renal angiomyolipoma? AJR Am J Roentgenol 201:1017–1028CrossRefPubMedGoogle Scholar

Copyright information

© European Society of Radiology 2015

Authors and Affiliations

  • Nicola Schieda
    • 1
  • Marc Dilauro
    • 1
  • Bardia Moosavi
    • 1
  • Taryn Hodgdon
    • 1
  • Gregory O. Cron
    • 1
  • Matthew D. F. McInnes
    • 1
  • Trevor A. Flood
    • 2
  1. 1.Department of Medical ImagingThe Ottawa HospitalOttawaCanada
  2. 2.Department of Anatomical PathologyThe Ottawa Hospital, The University of OttawaOttawaCanada

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