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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.
European Radiology 2016 July
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.
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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