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

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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.

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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.

Author information

Authors and Affiliations

  1. Department of Medical Imaging, The Ottawa Hospital, 1053 Carling Avenue, Ottawa, Ontario, Canada, K1Y 4E9

    Nicola Schieda, Marc Dilauro, Bardia Moosavi, Taryn Hodgdon, Gregory O. Cron & Matthew D. F. McInnes

  2. Department of Anatomical Pathology, The Ottawa Hospital, The University of Ottawa, 501 Smyth Road, 4th floor CCW, Room 4278, Ottawa, Ontario, Canada, K1Y 4E9

    Trevor A. Flood

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  1. Nicola Schieda
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Correspondence to Nicola Schieda.

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Schieda, N., Dilauro, M., Moosavi, B. et al. MRI evaluation of small (<4cm) solid renal masses: multivariate modeling improves diagnostic accuracy for angiomyolipoma without visible fat compared to univariate analysis. Eur Radiol 26, 2242–2251 (2016). https://doi.org/10.1007/s00330-015-4039-y

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  • DOI: https://doi.org/10.1007/s00330-015-4039-y

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