Qualitative and quantitative radiological analysis of non-contrast CT is a strong indicator in patients with acute pyelonephritis

Objective: To evaluate the performance of non-contrast computed tomography (CT) by reporting the difference in attenuation between normal and inflamed renal parenchyma in patients clinically diagnosed with acute pyelonephritis (APN). Material and methods: This is a retrospective study concerned with non-contrast CT evaluation of 74 patients, admitted with a clinical diagnosis of APN and failed to respond to 48 h antibiotics treatment. Mean attenuation values in Hounsfield units (HU) were measured in the upper, middle and lower segments of the inflamed and the normal kidney of the same patient. Independent t-test was performed for statistical analysis. Image evaluation included receiver operating characteristic (ROC), visual grading characteristic (VGC) and kappa analyses. Results: The mean attenuation in the upper, middle and lower segments of the inflamed renal cortex was 32%, 25%, and 29% lower than the mean attenuation of the corresponding cortical segments of the contralateral normal kidney, respectively (p < 0.01). The mean attenuation in the upper, middle, and lower segments of the inflamed renal medulla was 48%, 21%, and 30%, lower than the mean attenuation of the corresponding medullary segments of the contralateral normal kidney (p < 0.02). The mean attenuation between the inflamed and non-inflamed renal cortex and medulla was 29% and 30% lower respectively (p < 0.001). The AUCROC (p < 0.001) analysis demonstrated significantly higher scores for pathology detection, irrespective of image quality, compared to clinical and laboratory results with an increased inter-reader agreement from poor to substantial. Conclusion: Non-contrast CT showed a significant decrease in the parenchymal density of the kidney affected with APN in comparison to the contralateral normal kidney of the same patient. This can be incorporated in the diagnostic criteria of APN in NCCT in the emergency setting. (C) 2017 Elsevier Inc. All rights reserved.