Prediction of spontaneous ureteral stone passage: Automated 3D-measurements perform equal to radiologists, and linear measurements equal to volumetric

Johan Jendeberg,Håkan Geijer,Muhammed Alshamari,Mats Lidén

doi:10.1007/s00330-017-5242-9

Abstract

ObjectivesTo compare the ability of different size estimates to predict spontaneous passage of ureteral stones using a 3D-segmentation and to investigate the impact of manual measurement variability on the prediction of stone passage.MethodsWe retrospectively included 391 consecutive patients with ureteral stones on non-contrast-enhanced CT (NECT). Three-dimensional segmentation size estimates were compared to the mean of three radiologists’ measurements. Receiver-operating characteristic (ROC) analysis was performed for the prediction of spontaneous passage for each estimate. The difference in predicted passage probability between the manual estimates in upper and lower stones was compared.ResultsThe area under the ROC curve (AUC) for the measurements ranged from 0.88 to 0.90. Between the automated 3D algorithm and the manual measurements the 95% limits of agreement were 0.2 ± 1.4 mm for the width. The manual bone window measurements resulted in a > 20 percentage point (ppt) difference between the readers in the predicted passage probability in 44% of the upper and 6% of the lower ureteral stones.ConclusionsAll automated 3D algorithm size estimates independently predicted the spontaneous stone passage with similar high accuracy as the mean of three readers’ manual linear measurements. Manual size estimation of upper stones showed large inter-reader variations for spontaneous passage prediction.Key points• An automated 3D technique predicts spontaneous stone passage with high accuracy.• Linear, areal and volumetric measurements performed similarly in predicting stone passage.• Reader variability has a large impact on the predicted prognosis for stone passage.

Highlights

Ureteral stones are one of the most common causes of acute flank pain, with large and increasing costs for the health care [1, 2]
In this study we demonstrated that an automated segmentation algorithm performs to the mean of three readers’ manual measurements in predicting spontaneous ureteral stone passage, that linear size estimates perform to more complicated measurements and that relatively small inter-reader variability in the manual measurements of upper ureteral stones can cause large differences in the predicted probability of stone passage
Previous studies have shown that spontaneous passage of a ureteral calculus can be predicted with high accuracy with the knowledge of the calculus’ size and location [3, 4, 7]

Summary

Introduction

Ureteral stones are one of the most common causes of acute flank pain, with large and increasing costs for the health care [1, 2]. Earlier studies [3,4,5,6,7] have shown that about 80% of ureteral stones pass spontaneously into the urinary bladder. In the absence of complications, if the pain is manageable and the stone can be expected to pass within a reasonable time without surgical intervention, the first approach is conservative, with radiological and clinical surveillance [8]. If a stone is not expected to pass it is. To select an appropriate treatment strategy for each individual, prediction of the probability for spontaneous stone passage is important [9]. The correlation between stone size and position and the probability for spontaneous stone passage is strong [3,4,5, 7], but at present there is no international consensus on a standardized method of stone size measurement with noncontrast-enhanced computed tomography (NECT)

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