Dual-energy vs Conventional Computed Tomography in Determining Stone Composition

doi:10.1016/j.urology.2013.12.023

Urology

Volume 83, Issue 6, June 2014, Pages 1243-1247

https://doi.org/10.1016/j.urology.2013.12.023 Get rights and content

Objective

To compare the accuracy between conventional computed tomography (CT) and dual-energy CT (DECT) in predicting stone composition in a blinded, prospective fashion.

Methods

A total of 32 renal stones with known composition were scanned in vitro, first using standard CT techniques at 120 kilovolt peak (kV[p]) and then using fast-switched kilovolt DECT at 80 and 140 kilovolt peak (kV[p]). For the DECT scan, a spectral curve was created demonstrating the change of Hounsfield units (HU) across the kiloelectron volt spectrum. The composition of each stone was estimated by comparing each sample curve with curves of known materials. To attempt stone determination using single-energy CT, the HU of each stone was compared with ranges reported in previous studies. The accuracy of each method was compared.

Results

Included were 27 stones large enough to allow analysis. Single-energy measurements accurately identified 14 of 27 stones of all composition (52%), whereas the DECT spectral curves correctly identified 20 (74%). When analyzed by stone type, single-energy vs DECT correctly identified 12 vs 12 of the 12 uric acid stones, 2 vs 3 of the 6 struvite stones, 0 vs 3 of the 5 cystine stones, and 0 vs 2 of the 4 calcium oxalate stones, respectively. When simply attempting to differentiate uric acid vs nonuric acid stones, single-energy CT could accurately differentiate only 6 of 15 stones as nonuric acid (40%) compared with 14 of 15 stones (93%) for DECT.

Conclusion

DECT appears to be superior to conventional CT in differentiating stone composition and is particularly accurate in differentiating nonuric acid from uric acid stones.

Section snippets

Materials and Methods

Thirty-two renal stones with a known, pure composition, as determined by infrared spectroscopy, were placed in a water bath measuring 25 cm high × 40 cm wide to simulate the scatter from a normal body habitus. Stone size ranged from 1-10 mm and consisted of pure uric acid, cystine, struvite, and calcium oxalate monohydrate.

The stones were scanned using conventional imaging techniques at 120 kV(p) and subsequently using fast kV-switching DECT, using the same scanner (GE Healthcare Discovery 750

Results

Included were 27 stones large enough to allow a ROI that fit completely within the stone. Stones sized <3 mm could not fit the ROI and were therefore excluded. The size and composition of each stone is summarized in Table 1. The mean attenuation values of each stone subtype compared with the reference range are reported in Table 2.

The accuracy for each stone phenotype is compared in Figure 2. For single-energy measurements, the blinded clinician accurately identified 14 of 27 stones of all

Comment

Knowledge of a stone's composition can be a decisive factor in determining optimal management, yet there is no reliable method of determining composition until the stone has been retrieved or passed. Ideally, knowledge of stone phenotype before treatment would allow optimal management and thus improve patient outcomes. This may include treating uric acid stones with medical dissolution therapy, which has reported success rates as high as 70%-80%⁹ and could spare patients from potentially

Conclusion

When compared in a blinded, prospective fashion, DECT performed superiorly to conventional single-energy CT in classifying calcium oxalate, cystine, struvite, and uric acid stones. In addition, there appears to be a distinct advantage in differentiating nonuric acid from uric acid stones.

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First, the automated approach reduced the time required to classify urinary stones compared to conventional methods. Second, urinary stone analysis using infrared spectroscopy (FT-IR), XRD, and transmission electron microscopy (TEM) requires no prior preparation of samples [10]. Third, the proposed automated approach could be used to determine the composition of mixture stones.
The classification of urinary stones is important prior to treatment because the treatments depend on three types of urinary stones, i.e., calcium, uric acid, and mixture stones. We have developed an automatic approach for the classification of urinary stones into the three types based on microcomputed tomography (micro-CT) images using a convolutional neural network (CNN).
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The proposed automated CNN-based approach could successfully classify urinary stones into three types, namely calcium, uric acid, and mixture stones, using micro-CT images.
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DECT is a newer technique that was reported to accurately discriminate between different stone compositions.16,17 In an in vitro study Wisenbaugh et al noted that DECT could more accurately classify UA stones than single energy CT (93% vs 40%).18 In a recent in vivo study in 30 patients DECT accurately determined stone composition in 90% with 100% sensitivity, 94% specificity, 100% positive predictive value and 96% negative predictive value to predict a UA stone.19
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A total of 100 patients, of whom 52 had calcium oxalate and 48 had uric acid stones, were included in the final analysis. Uric acid stones were significantly larger (12.2 vs 9.0 mm, p = 0.03) but calcium oxalate stones had higher mean attenuation (457 vs 315 HU, p = 0.001) and maximum attenuation (918 vs 553 HU, p <0.001). Kurtosis was significantly higher in each axis for calcium oxalate stones (each p <0.001). A composite algorithm using attenuation distribution pattern, average attenuation and stone size had overall 89% sensitivity, 91% specificity, 91% positive predictive value and 89% negative predictive value to predict uric acid stones.
A combination of stone size, attenuation intensity and attenuation pattern from conventional computerized tomography can distinguish uric acid stones from calcium oxalate stones with high sensitivity and specificity.
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Endourology and StonesDual-energy vs Conventional Computed Tomography in Determining Stone Composition

Objective

Methods

Results

Conclusion

Section snippets

Materials and Methods

Results

Comment

Conclusion

Urol Clin North Am

Adv Chronic Kidney Dis

Urology

J Urol

Urology

Eur J Radiol

Endourology and Stones
Dual-energy vs Conventional Computed Tomography in Determining Stone Composition