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Pure uric acid stone prediction model using the variant coefficient of stone density measured by thresholding 3D segmentation-based methods: A multicenter study

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Author(s)
Bong-Il SongJinny LeeWonho JungBum Soo Kim
Keimyung Author(s)
Song, Bong IlJung, Won Ho
Department
Dept. of Nuclear Medicine (핵의학)
Dept. of Urology (비뇨의학)
Journal Title
Comput Methods Programs Biomed
Issued Date
2023
Volume
240
Keyword
Noncontrast computed tomographyPredictionPure uric acid stoneSegmentationVariant coefficient of stone density
Abstract
Urinary stones are common urological diseases with increasing prevalence and incidence worldwide. Among the various types of stones, uric acid stones can be dissolved by oral chemolysis without any surgical procedure. Therefore, our study demonstrates that variant coefficient of stone density measured by thresholding a three-dimensional segmentation-based method from noncontrast computed tomography images can be used to identify pure uric acid stones from non-pure uric acid stones. This study provides a preoperative pure uric acid stone prediction model that could reduce invasive procedural treatments. The pure uric acid stone prediction model may offer optimized clinical decision-making for patients with urinary stones.

Background and objectives:
While most urinary stones are managed with interventional therapy, uric acid (UA) stones can be dissolved by oral chemolysis without invasive procedures. This study aimed to develop and validate a pure UA (pUA) stone prediction model using a variant coefficient of stone density (VCSD) measured by thresholding a three-dimensional (3D) segmentation-based method.

Methods:
Patients with urolithiasis treated at Keimyung University Dongsan Hospital between January 2017 and December 2020 were divided into training and internal validation sets, and patients from Kyungpook National University Hospital between January 2017 and December 2018 were used as an external validation set. Each stone was segmented by a thresholding 3D segmentation-based method using an attenuation threshold of 130 Hounsfield units. VCSD was calculated as the stone heterogeneity index divided by the mean stone density.

Results:
A total of 1175 urinary stone cases in 1023 patients were enrolled in this study. Of these, 224 (19.1%) were pUA stone cases. Among the potential predictors, thresholding 3D segmentation-based VCSD, age, sex, radio-opacity, hypertension, diabetes, and urine pH were identified as independent pUA stone predictors, and VCSD was the most powerful indicator. The pUA stone prediction model showed good discrimination, yielding area under the receiver operating characteristic curve of 0.960 (95% confidence interval (CI): 0.940-0.979, P < 0.001), 0.931 (95% CI: 0.875-0.987, P < 0.001), and 0.938 (95% CI: 0.912-0.965, P < 0.001) in the training, internal validation, and external validation sets, respectively.

Conclusions:
VCSD measured using 3D segmentation was a decisive independent predictive factor for pUA stones. Furthermore, the established prediction model with VCSD can serve as a noninvasive preoperative tool to identify pUA stones.
Keimyung Author(s)(Kor)
송봉일
정원호
Publisher
School of Medicine (의과대학)
Type
Article
ISSN
1872-7565
Source
https://www.clinicalkey.com/#!/content/playContent/1-s2.0-S0169260723003565
DOI
10.1016/j.cmpb.2023.107691
URI
https://kumel.medlib.dsmc.or.kr/handle/2015.oak/45196
Appears in Collections:
1. School of Medicine (의과대학) > Dept. of Nuclear Medicine (핵의학)
1. School of Medicine (의과대학) > Dept. of Urology (비뇨의학)
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