Rock Solid Measurements: A Comparison of Three Methods of Kidney Stone Volume Assessment

Abstract

Introduction and Objective:

Renal stone volume is an important variable for treatment selection and predicting surgical outcomes. However, the standardized and optimal method for stone volume assessment has not been identified. The purpose of this study was to evaluate and compare three methods of volume assessment: the scalene ellipsoid formula, three-dimensional (3D) slicer, and Enterprise Imaging.

Methods:

A bench top model was created including a torso and kidney based on an actual patient computed tomography (CT). Five kidney stone sets of varying sizes were implanted and scanned using a 64 slice CT scanner. Ten blinded reviewers used the scalene ellipsoid formula, 3D slicer, and Enterprise Imaging to measure kidney stone volume for each stone set. Using these measurements, the methods were compared for inter-rater reliability, accuracy, speed, validity, and convenience. Significance was determined by the Friedman test in addition to using Tukey’s post hoc and analysis of variance where appropriate.

Results:

All three techniques had high inter-rater reliability (intraclass correlation coefficient >0.98). The mean relative error for Enterprise Imaging (4.9%) was significantly lower than 3D slicer (10.2%; p < 0.001) and the scalene ellipsoid formula (43.8%; p < 0.001). When performed by an expert, Enterprise Imaging (17.8 seconds) was faster than the scalene ellipsoid formula (25.2 seconds; p = 0.006) and both were faster than 3D slicer (196.6 seconds; p < 0.001). Each method was determined to be internally consistent and valid (α > 0.9; R ² > 0.98, respectively). Enterprise Imaging was determined to be significantly more convenient (p < 0.001) than both the scalene ellipsoid formula and 3D slicer.

Conclusion:

In this study, Enterprise Imaging was a more accurate and efficient tool to measure stone volume. Clinicians can utilize Enterprise Imaging to efficiently determine stone volume and to better select treatment and predict operative and postoperative outcomes.