Literature DB >> 27554618

Prospective prediction of the major component of urinary stone composition with dual-source dual-energy CT in vivo.

G-M-Y Zhang1, H Sun2, H-D Xue1, H Xiao3, X-B Zhang3, Z-Y Jin4.   

Abstract

AIM: To prospectively evaluate the diagnostic accuracy of dual-source dual-energy computed tomography (DSDECT) for predicting the major component and determining the composition of urinary calculi in patients with urolithiasis, using postoperative in vitro Fourier transform infrared spectroscopy (FT-IR) analysis as the reference standard.
MATERIALS AND METHODS: Patients with known urolithiasis underwent preoperative DSDECT evaluation, and subsequently, underwent surgical removal of the stones. All patients were examined using the dual-energy renal stone protocol. Material-specific chromatic images were made using dedicated post-processing software. The final determination of stone composition was made using FT-IR postoperatively. Diagnostic parameters of DSDECT for predicting the major component and detecting the presence of four composition types were calculated.
RESULTS: A total of 81 urinary calculi were included in this study. Forty-three were pure stones and 38 were mixed stones according to FT-IR. DSDECT correctly identified the major component of all pure stones and 36 mixed stones. The major component of two mixed stones with uric acid as the major component was falsely interpreted as calcium oxalate. The overall accuracy of DSDECT for predicting the major component of stones was 97.5% (79/81). The accuracy of DSDECT for detecting the presence of four types of composition, uric acid, cysteine, hydroxyapatite, and calcium oxalate, was 97.5% (79/81), 93.8% (76/81), 80.2% (65/81), and 93.8% (76/81), respectively.
CONCLUSION: DSDECT could accurately predict the major component of urinary calculi and detect uric acid, cysteine, and calcium oxalate with a satisfactory accuracy.
Copyright © 2016 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

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Year:  2016        PMID: 27554618     DOI: 10.1016/j.crad.2016.07.012

Source DB:  PubMed          Journal:  Clin Radiol        ISSN: 0009-9260            Impact factor:   2.350


  5 in total

1.  CT-calculometry (CT-CM): advanced NCCT post-processing to investigate urinary calculi.

Authors:  Valentin Zumstein; Patrick Betschart; Lukas Hechelhammer; Hans-Peter Schmid; Dominik Abt; Magdalena Müller-Gerbl
Journal:  World J Urol       Date:  2017-09-25       Impact factor: 4.226

2.  Prediction of the Uric Acid Component in Nephrolithiasis Using Simple Clinical Information about Metabolic Disorder and Obesity: A Machine Learning-Based Model.

Authors:  Hao-Wei Chen; Yu-Chen Chen; Jung-Ting Lee; Frances M Yang; Chung-Yao Kao; Yii-Her Chou; Ting-Yin Chu; Yung-Shun Juan; Wen-Jeng Wu
Journal:  Nutrients       Date:  2022-04-27       Impact factor: 6.706

3.  Diagnostic accuracy of third-generation dual-source dual-energy CT: a prospective trial and protocol for clinical implementation.

Authors:  Tim Nestler; Kai Nestler; Andreas Neisius; Hendrik Isbarn; Christopher Netsch; Stephan Waldeck; Hans U Schmelz; Christian Ruf
Journal:  World J Urol       Date:  2018-08-03       Impact factor: 4.226

Review 4.  [Update of the 2Sk guidelines on the diagnostics, treatment and metaphylaxis of urolithiasis (AWMF register number 043-025) : What is new?]

Authors:  C Seitz; T Bach; M Bader; W Berg; T Knoll; A Neisius; C Netsch; M Nothacker; S Schmidt; M Schönthaler; R Siener; R Stein; M Straub; W Strohmaier; C Türk; B Volkmer
Journal:  Urologe A       Date:  2019-11       Impact factor: 0.639

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

Authors:  Lei Tang; Wuchao Li; Xianchun Zeng; Rongpin Wang; Xiushu Yang; Guangheng Luo; Qijian Chen; Lihui Wang; Bin Song
Journal:  Ann Transl Med       Date:  2021-07
  5 in total

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