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Updated: Feb 28, 2026
In Vivo, Percutaneous, Needle Based, Optical Coherence Tomography of Renal Masses
Published on: March 30, 2015
Stepwise algorithm using computed tomography and magnetic resonance imaging for diagnosis of fat-poor angiomyolipoma
Hajime Tanaka1, Yasuhisa Fujii1, Hiroshi Tanaka2
1Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan.
Objectives:
To develop a stepwise diagnostic algorithm for fat-poor angiomyolipoma in small renal masses.
Methods:
Two cohorts of small renal masses <4 cm without an apparent fat component that was pathologically diagnosed were included: 153 cases (18 fat-poor angiomyolipomas/135 renal cell carcinomas) for model development and 71 cases (seven fat-poor angiomyolipomas/59 renal cell carcinomas/5 oncocytomas) for validation. Dynamic contrast-enhanced computed tomography, magnetic resonance imaging and clinical findings were analyzed. Based on multivariate analysis, we developed two prediction models for fat-poor angiomyolipoma, the computed tomography model and the computed tomography + magnetic resonance imaging model, and a stepwise algorithm that proposes the sequential use of computed tomography and magnetic resonance imaging.
Results:
The computed tomography model, which was composed of female aged <50 years, high attenuation on unenhanced computed tomography, less enhancement than the normal renal cortex and homogeneity in the corticomedullary phase, differentiated tumors with none of the factors as the low angiomyolipoma-probability group, and the others were candidates for the computed tomography + magnetic resonance imaging model. The computed tomography + magnetic resonance imaging model, consisting of the first three factors of the computed tomography model, low signal intensity and absence of pseudocapsule on T2-weighted magnetic resonance imaging, re-stratified the tumors into low, intermediate and high angiomyolipoma-probability groups. The incidence of fat-poor angiomyolipoma in each group was 0%, 26% and 93%, respectively (area under the curve 0.981). External validation by two readers showed a high area under the curve (0.912 and 0.924) for each. The interobserver agreement was good (kappa score 0.77).
Conclusions:
The present algorithm differentiates fat-poor angiomyolipoma in small renal masses with high accuracy by adding magnetic resonance imaging to computed tomography in selected patients.
