CT differentiation of fat-poor angiomyolipomas from papillary renal cell carcinomas: development of a predictive model

• Published in: Abdominal imaging Vol. 46; no. 7; pp. 3280 - 3287
• Main Authors: Salvador, R., Sebastià, M., Cárdenas, G., Páez-Carpio, A., Paño, B., Solé, M., Nicolau, C.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2021; Springer Nature B.V
• Subjects: Attenuation; Bladder; Blood vessels; Demographic variables; Gastroenterology; Gender; Hepatology; Imaging; Kidney cancer; Kidneys; Medical diagnosis; Medicine; Medicine & Public Health; Papillary renal cell carcinoma; Prediction models; Radiology; Regression models; Retroperitoneum; Statistical analysis; Tomography; Tumors; Ureters; Angiomyolipoma; Renal cell carcinoma; Papillary renal cell carcinoma; Multidetector computed tomography
• ISSN: 2366-004X; 2366-0058
• DOI: 10.1007/s00261-021-02988-y

Purpose To identify specific contrast-enhanced CT (CECT) findings and develop a predictive model with logistic regression to differentiate fat-poor angiomyolipomas (fpAML) from papillary renal cell carcinomas (pRCC). Methods This is a single-institution retrospective study that assess CT features of histologically proven 67 pRCC and 13 fpAML. CECT variables were studied by means of univariate logistic regression. Variables included patients’ demographics, tumor attenuation (unenhanced and at arterial, venous and excretory post-contrast phases), type of enhancement, morphological features (axial long and short diameters, long-short axis ratio (LSR) and tumor to kidney angle interface) and presence of visible calcifications or vessels. Those variables with a p  ≤ 0.05 underwent standard stepwise logistic regression to find predictive combinations of clinical variables. Best models were evaluated by AUROC curves and were subjected to Leave-one-out cross validation to assess their robustness. Results Odds ratio (OR) between pRCC and fpAML was statistically significant for patient’s gender, tumor attenuation in arterial, venous and excretory phases, tumor’s long diameter, short diameter, LSR, type of enhancement, presence of intratumoral vessels and tumor-kidney angle interface. The best predictive model resulted in an area under the curve (AUC) of 0.971 and included gender, tumor-kidney angle interface and venous attenuation with the following equation: Log( p /1 −  p ) = − 2.834 + 4.052 * gender +  − 0.066 * AngleInterface + 0.074 * VenousphaseHU. Conclusions The combination of patients’ gender, tumor to kidney angle interface and venous enhancement helps to distinguish fpAML from pRCC.