Dual-Energy Computed Tomography for Stone Type Assessment: A Pilot Study of Dual-Energy Computed Tomography with Five Indices

Rudenko, V I; Серова, Н.С.; Kapanadze, L.B.; Taratkin, Mark; Okhunov, Zhamshid; Leonard, Stephen Pax; Ritter, Manuel; Kriegmair, Maximilian C.; Snurnitsyna, Olesya; Kozlov, Vasiliy; Laukhtina, Ekaterina; Arshiev, Magomed; Aleksandrova, K A; Salomon, Georg; Enikeev, Dmitry; Glybochko, Petr

doi:10.1089/end.2020.0243

Cited by 9 publications

(5 citation statements)

References 24 publications

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“…Rudenko et al report that dual-energy CT could evaluate the chemical composition of all stone types with speci city and accuracy ranging between 85% and 100% using the Z eff value and other parameters. [13] Material separation means that the spectral image is transformed into images of the base-pairs materials by attenuation measurements and mathematical methods. The water/iodine-attenuating base-pair images usually form the basis of this approach.…”

Section: Discussionmentioning

confidence: 99%

The characterization of in-vivo urinary infection stones by spectral CT

Huang

Ouyang

et al. 2022

Preprint

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Purpose To analyze characteristics of urinary infection stones using spectral CT (rapid kV switching dual-energy CT) in vivo. Methods This study included 55 single-composition and 36 mixed-composition infection stones, derived from 38 patients (15 males and 23 females), median age 49 (range, 10–71) years. We detected and compared effective nuclear number (Zeff) peak, its high and low points, average Zeff value, water- and iodine-based values, and average CT values. Post-surgical infrared spectroscopy results were compared with CT findings. Results In single-composition stones, 21 were of carbapatite, 20 of struvite, and 14 of brushite. These differed significantly in Zeff peak, its low and high points, average Zeff value, and iodine-based value. Three compositions had similar water-based and average CT values. The mixed-composition stones all had two Zeff peaks, and Zeff peaks in 36 mixed-composition stones included 23 of carbapatite, 25 of struvite, and 24 of brushite. When 95% confidence intervals of Zeff peaks in single-composition stones were as standard, they overlapped 82.6% of carbapatite, 88.0% of struvite, and 87.5% of brushite in mixed-composition stones. But many low and high points in three mixed-composition groups were outside the 95% confidence interval (overlap < 70%). Differences in water/iodine-based, average Zeff and average CT values did not reach the significance level. Conclusion Zeff peaks of urinary infection stones in vivo spectral CT revealed characteristics of various infective compositions.

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Section: Discussionmentioning

confidence: 99%

The characterization of in-vivo urinary infection stones by spectral CT

Huang

Ouyang

et al. 2022

Preprint

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“…With the introduction of commercially available dual-energy CT scanners, 53 it is nowadays possible to identify differences in tissue composition. 54 This feature may aid to distinguish in routine clinical exams these two different types of calcium. The capability of differentiating between calcium subtypes may further improve our understanding of the unique roles played by each type of calcium in predicting stroke risk.…”

Section: The Calcium Problemmentioning

confidence: 99%

Carotid Artery Plaque Calcifications: Lessons From Histopathology to Diagnostic Imaging

Saba

Nardi

Cau

et al. 2022

Stroke

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The role of calcium in atherosclerosis is controversial and the relationship between vascular calcification and plaque vulnerability is not fully understood. Although calcifications are present in ≈50% to 60% of carotid plaques, their association with cerebrovascular ischemic events remains unclear. In this review, we summarize current understanding of carotid plaque calcification. We outline the role of calcium in atherosclerotic carotid disease by analyzing laboratory studies and histopathologic studies, as well as imaging findings to understand clinical implications of carotid artery calcifications. Differences in mechanism of calcium deposition express themselves into a wide range of calcification phenotypes in carotid plaques. Some patterns, such as rim calcification, are suggestive of plaques with inflammatory activity with leakage of the vasa vasourm and intraplaque hemorrhage. Other patterns such as dense, nodular calcifications may confer greater mechanical stability to the plaque and reduce the risk of embolization for a given degree of plaque size and luminal stenosis. Various distributions and patterns of carotid plaque calcification, often influenced by the underlying systemic pathological condition, have a different role in affecting plaque stability. Modern imaging techniques afford multiple approaches to assess geometry, pattern of distribution, size, and composition of carotid artery calcifications. Future investigations with these novel technologies will further improve our understanding of carotid artery calcification and will play an important role in understanding and minimizing stroke risk in patients with carotid plaques.

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“…Ilyas et al (25) analyzed 60 stones in 53 patients and found that the sensitivity and specificity of DECT to differentiate UA and non-UA stones were both 100%, and 97.8% and 92.3% for CO and non-CO stones. Many studies have shown that DECT can distinguish UA and calcified stones in vivo, with an accuracy of up to 100%, and has potential for detection of mixed stones (26,27). However, compared with unenhanced CT, the cost of DECT equipment and examinations are more expensive, the clinical promotion is insufficient, and it cannot be carried out as an emergency program.…”

Section: Determination Of Stone Composition With Ctmentioning

confidence: 99%

Value of artificial intelligence model based on unenhanced computed tomography of urinary tract for preoperative prediction of calcium oxalate monohydrate stones in vivo

Tang¹,

Li²,

Zeng³

et al. 2021

Ann Transl Med

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Background: Urolithiasis is a global disease with a high incidence and recurrence rate, and stone composition is closely related to the choice of treatment and preventive measures. Calcium oxalate monohydrate (COM) is the most common in clinical practice, which is hard and difficult to fragment.Preoperative identification of its components and selection of effective surgical methods can reduce the risk of patients having a second operation. Methods that can be used for stone composition analysis include infrared spectroscopy, X-ray diffraction, and polarized light microscopy, but they are all performed on stone specimens in vitro after surgery. This study aimed to design and develop an artificial intelligence (AI) model based on unenhanced computed tomography (CT) images of the urinary tract, and to investigate the predictive ability of the model for COM stones in vivo preoperatively, so as to provide surgeons with more accurate diagnostic information.Methods: Preoperative unenhanced CT images of patients with urinary calculi whose components were determined by infrared spectroscopy in a single center were retrospectively analyzed, including 337 cases of COM stones and 170 of non-COM stones. All images were manually segmented and the image features were extracted, and randomly divided into the training and testing sets in a ratio of 7:3. The least absolute shrinkage and selection operation algorithm (LASSO) was used to construct the AI model, and classification of the training and testing sets was carried out.Results: A total of 1,218 radiomics imaging features were extracted, and 8 features with non-zero coefficients were finally obtained. The sensitivity, specificity and accuracy of the AI model were 90.5%, 84.3% and 88.5% for the training set, and 90.1%, 84.3% and 88.3% for the testing set. The area under the curve was 0.935 for the training set and 0.933 for the testing set. Conclusions:The AI model based on unenhanced CT images of the urinary tract can predict COM and non-COM stones in vivo preoperatively, and the model has high sensitivity, specificity and accuracy.

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Dual-Energy Computed Tomography for Stone Type Assessment: A Pilot Study of Dual-Energy Computed Tomography with Five Indices

Cited by 9 publications

References 24 publications

The characterization of in-vivo urinary infection stones by spectral CT

The characterization of in-vivo urinary infection stones by spectral CT

Carotid Artery Plaque Calcifications: Lessons From Histopathology to Diagnostic Imaging

Value of artificial intelligence model based on unenhanced computed tomography of urinary tract for preoperative prediction of calcium oxalate monohydrate stones in vivo

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