Naoki Takahashi1, Shuai Leng1, Kazuhiro Kitajima1,2, Daniel Gomez-Cardona1,3, Prabin Thapa4, Rickey E Carter4, Bradley C Leibovich5, Kewalee Sasiwimonphan1,6, Kohei Sasaguri1,7, Akira Kawashima1. 1. 1 Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905. 2. 2 Present address: Department of Radiology, Kobe University, Faculty of Medicine, Hyogo, Japan. 3. 3 Present address: Department of Medical Physics, School of Medicine, University of Wisconsin, Madison, WI. 4. 4 Department of Health Sciences Research, Mayo Clinic, Rochester, MN. 5. 5 Department of Urology, Mayo Clinic, Rochester, MN. 6. 6 Present address: Department of Radiology, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Chulalongkorn University, Bangkok, Thailand. 7. 7 Present address: Department of Radiology, Faculty of Medicine, Saga University, Saga, Japan.
Abstract
OBJECTIVE: The purpose of this study was to evaluate if small (< 4 cm) angiomyolipoma without visible fat can be differentiated from renal cell carcinoma (RCC) using contrast-enhanced CT alone and using unenhanced and contrast-enhanced CT in combination. MATERIALS AND METHODS: Twenty-three patients with 24 angiomyolipomas without visible fat and 130 patients with 148 RCCs underwent unenhanced and contrast-enhanced CT. Demographic data and size, shape, CT attenuation, and heterogeneity (entropy and subjective score) of the renal mass on unenhanced CT and contrast-enhanced CT were recorded. Multivariate logistic regression models were constructed for parameters obtained by contrast-enhanced CT alone and by both unenhanced and contrast-enhanced CT. Demographic data and size and shape of renal mass were used in each model. Sensitivity and specificity were calculated. RESULTS: Logistic regression model from contrast-enhanced CT data included sex, percentage of exophytic growth, entropy, and CT attenuation on contrast-enhanced CT. Model from both unenhanced and contrast-enhanced CT data included age, sex, short-axis diameter, percentage of exophytic growth, lesion-to-kidney CT attenuation difference on unenhanced CT, and CT attenuation on contrast-enhanced CT. The contrast-enhanced CT-based model and combined unenhanced and contrast-enhanced CT-based model differentiated angiomyolipoma from RCC with sensitivity and specificity of 42% and 98% versus 50% and 98%, respectively. CONCLUSION: Combinations of various CT and demographic findings allowed differentiation of angiomyolipoma from RCC.
OBJECTIVE: The purpose of this study was to evaluate if small (< 4 cm) angiomyolipoma without visible fat can be differentiated from renal cell carcinoma (RCC) using contrast-enhanced CT alone and using unenhanced and contrast-enhanced CT in combination. MATERIALS AND METHODS: Twenty-three patients with 24 angiomyolipomas without visible fat and 130 patients with 148 RCCs underwent unenhanced and contrast-enhanced CT. Demographic data and size, shape, CT attenuation, and heterogeneity (entropy and subjective score) of the renal mass on unenhanced CT and contrast-enhanced CT were recorded. Multivariate logistic regression models were constructed for parameters obtained by contrast-enhanced CT alone and by both unenhanced and contrast-enhanced CT. Demographic data and size and shape of renal mass were used in each model. Sensitivity and specificity were calculated. RESULTS: Logistic regression model from contrast-enhanced CT data included sex, percentage of exophytic growth, entropy, and CT attenuation on contrast-enhanced CT. Model from both unenhanced and contrast-enhanced CT data included age, sex, short-axis diameter, percentage of exophytic growth, lesion-to-kidney CT attenuation difference on unenhanced CT, and CT attenuation on contrast-enhanced CT. The contrast-enhanced CT-based model and combined unenhanced and contrast-enhanced CT-based model differentiated angiomyolipoma from RCC with sensitivity and specificity of 42% and 98% versus 50% and 98%, respectively. CONCLUSION: Combinations of various CT and demographic findings allowed differentiation of angiomyolipoma from RCC.
Authors: Robert S Lim; Matthew D F McInnes; Mahadevaswamy Siddaiah; Trevor A Flood; Luke T Lavallee; Nicola Schieda Journal: Eur Radiol Date: 2018-02-28 Impact factor: 5.315
Authors: Tyler M Bauman; Aaron M Potretzke; Alec J Wright; Joel M Vetter; Theodora A Potretzke; R Sherburne Figenshau Journal: Investig Clin Urol Date: 2017-06-27