Nicola Schieda1, Marc Dilauro2, Bardia Moosavi2, Taryn Hodgdon2, Gregory O Cron2, Matthew D F McInnes2, Trevor A Flood3. 1. Department of Medical Imaging, The Ottawa Hospital, 1053 Carling Avenue, Ottawa, Ontario, Canada, K1Y 4E9. nschieda@toh.on.ca. 2. Department of Medical Imaging, The Ottawa Hospital, 1053 Carling Avenue, Ottawa, Ontario, Canada, K1Y 4E9. 3. Department of Anatomical Pathology, The Ottawa Hospital, The University of Ottawa, 501 Smyth Road, 4th floor CCW, Room 4278, Ottawa, Ontario, Canada, K1Y 4E9.
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.
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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