Ganesan V, et al. - This paper intended to formulate a software algorithm that could utilize data from non-contrast computed tomography (CT) to speculate the presence of uric acid (UA) stones. It was determined that a combination of stone size, attenuation intensity and attenuation pattern from conventional CT imaging aided in discerning UA stones from calcium oxalate (CaOx) stones with high sensitivity and specificity.

Methods

• The recruitment consisted of patients with pure uric acid (UA) and calcium oxalate (CaOx) stones, identified from the stone registry.
• The inclusion criteria inciolved just the stones >4 mm and clearly traceable from initial CT to final composition.
• A semi-automated, computer algorithm processed the image data.
• Hounsfield unit (HU) averages, maximums, eccentricity (deviation from circle), and kurtosis (“peakedness” versus “flatness”) were automatically generated.
• Several mathematical models inspected these parameters to speculate the presence of UA stones.

Results

• This study examined 100 patients, 52 with CaOx and 48 with UA stones. UA stones were considerably larger (9.0 mm vs 12.2 mm, p=0.03).
• CaOx stones, however, displayed both higher mean (457 HU vs. 315 HU, p = 0.001), and maximum attenuations (918 vs. 553 HU, p<0.001).
• Markedly higher Kurtosis was found in both axes for calcium oxalate stones (both p < 0.001).
• An overall sensitivity of 89%, specificity of 91%, PPV 91% and NPV 89% for predicting of UA stone was disclosed in a composite algorithm using attenuation distribution pattern, average attenuation, and stone size.