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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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.
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 %.
MRI, using multi-variate modelling, is accurate for diagnosis of AMLwvf.
• 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.
KeywordsAngiomyolipoma Magnetic resonance imaging Minimal fat T2 weighted imaging Contrast enhanced
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.
- 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
- 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
- 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
- 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
- 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
- 25.Ramamurthy NK, Moosavi B, McInnes MD, Flood TA, Schieda N (2014) Multiparametric MRI of solid renal masses: pearls and pitfalls. Clin RadiolGoogle Scholar
- 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