Purpose: To assess whether morphologic analysis using computed tomography (CT) could differentiate between fat-poor angiomyolipoma (fpAML) and renal cell carcinoma (RCC). Methods: A total of 602 patients with a histologically confirmed fpAML (n = 49) or RCC (n = 553) were evaluated. All renal lesions were less than 4 cm in size and had no gross fat on contrast-enhanced CT. For morphologic analysis, overflowing beer sign and angular interface were evaluated. Overflowing beer sign was defined as contact length between bulging-out portion of a mass and the adjacent renal capsule of 3 mm or greater. Angular interface was defined as the angle of parenchymal portion of a mass of 90° or less. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were assessed. Multivariate analysis was conducted to determine which variable is predictive of fpAML. Results: Sensitivity, specificity, PPV, NPV, and accuracy were 61.2% (30/49), 97.1% (537/553), 65.2% (30/46), 96.6% (537/556), and 94.2% (567/602) with overflowing beer sign, while they were 55.1% (27/49), 81.9% (453/553), 21.3% (27/127), 95.4% (453/475), and 79.7% (480/602) with angular interface for fpAML, respectively. Both CT variables were predictive of fpAML (overflowing beer sign, odds ratio = 132.881, p < 0.001; angular interface, odds ratio = 5.766, p = 0.010). The multivariate model with CT variables showed good performance for predicting fpAML (AUC, 0.871 with angular interface, 0.943 with overflowing beer sign, and 0.949 with both). Conclusion: Morphologic analysis with contrast-enhanced CT may be useful for differentiating fpAML from RCC. Overflowing beer sign has the potential as an imaging biomarker for fpAML.
Bibliographical noteFunding Information:
This research was supported by TAEJOON PHARM Co. Ltd., South Korea. There is no relevant financial conflict of interest. The authors would like to thank Dong-Su Jang, MFA, (Medical Illustrator, Medical Research Support Section, Yonsei University College of Medicine, Seoul, Korea) for his help with the illustrations. None.
© 2017, Springer Science+Business Media, LLC.
All Science Journal Classification (ASJC) codes
- Radiological and Ultrasound Technology
- Radiology Nuclear Medicine and imaging