Literature DB >> 24726314

Predicting urinary stone composition based on single-energy noncontrast computed tomography: the challenge of cystine.

Fabio Cesar Miranda Torricelli1, Giovanni Scala Marchini2, Shubha De3, Kleiton G R Yamaçake2, Eduardo Mazzucchi2, Manoj Monga4.   

Abstract

OBJECTIVE: To study several measurements from a single-energy noncontrast computed tomography (NCCT) that may distinguish calcium oxalate, uric acid, and cystine stones.
METHODS: Patients with pure urinary stones who had at least 1 single-energy NCCT before the stone composition analysis from January 2008 to December 2012 were enrolled in this study. The analyzed data comprised stone size, volume, core Hounsfield unit (HU), periphery HU, absolute and relative HU differences between core and periphery, and HU density. After these measurements, an NCCT bone window was subjectively evaluated to study the homogeneity of each stone from core to periphery. The Spearman correlation test was used to determine the correlation between HU values and stone size and volume for each group.
RESULTS: A total of 113 patients were found with pure urinary stones who also had a corresponding NCCT. There were 36, 47, and 30 patients in the calcium oxalate, uric acid, and cystine groups, respectively. The core HU, periphery HU, absolute and relative HU differences, and HU density were significantly different among the 3 groups (P<.001). Stone size and volume had a positive correlation with core and periphery HUs only for calcium oxalate and cystine stones. The subjective evaluation of the urinary calculi revealed a different pattern for each stone composition.
CONCLUSION: Single-energy NCCT may predict calcium oxalate stones with a high degree of accuracy. There is an overlap in radiographic profiles of cystine and uric acid stones, making a definitive differentiation more challenging.
Copyright © 2014 Elsevier Inc. All rights reserved.

Entities:  

Mesh:

Substances:

Year:  2014        PMID: 24726314     DOI: 10.1016/j.urology.2013.12.066

Source DB:  PubMed          Journal:  Urology        ISSN: 0090-4295            Impact factor:   2.649


  10 in total

Review 1.  Usefulness of hounsfield unit and density in the assessment and treatment of urinary stones.

Authors:  Adnan Gücük; Uğur Uyetürk
Journal:  World J Nephrol       Date:  2014-11-06

2.  A novel method for prediction of stone composition: the average and difference of Hounsfield units and their cut-off values.

Authors:  Serdar Celik; Ertugrul Sefik; Ismail Basmacı; Ibrahim Halil Bozkurt; Mehmet Erhan Aydın; Tarık Yonguc; Tansu Degirmenci
Journal:  Int Urol Nephrol       Date:  2018-07-06       Impact factor: 2.370

3.  The combination of mean and maximum Hounsfield Unit allows more accurate prediction of uric acid stones.

Authors:  Long Qin; Jianhua Zhou; Wei Hu; Hu Zhang; Yunhui Tang; Mingyong Li
Journal:  Urolithiasis       Date:  2022-06-06       Impact factor: 2.861

Review 4.  How should patients with cystine stone disease be evaluated and treated in the twenty-first century?

Authors:  Kim Hovgaard Andreassen; Katja Venborg Pedersen; Susanne Sloth Osther; Helene Ulrik Jung; Søren Kissow Lildal; Palle Joern Sloth Osther
Journal:  Urolithiasis       Date:  2015-11-27       Impact factor: 3.436

5.  Computerized tomography attenuation values can be used to differentiate hydronephrosis from pyonephrosis.

Authors:  Emrah Yuruk; Murat Tuken; Suhejb Sulejman; Aykut Colakerol; Ege Can Serefoglu; Kemal Sarica; Ahmet Yaser Muslumanoglu
Journal:  World J Urol       Date:  2016-07-01       Impact factor: 4.226

6.  Role of Stone Heterogeneity Index in Determining Success of Shock Wave Lithotripsy in Urinary Calculi.

Authors:  Nadeem Iqbal; Aisha Hasan; Ahsan Nazar; Sajid Iqbal; Mohammad Haroon Hassan; Behzad Saeed Gill; Rabiyya Khan; Saeed Akhter; Rodrigo Suarez-Ibarrola
Journal:  J Clin Transl Res       Date:  2021-03-24

7.  Clinical utility of computed tomography Hounsfield characterization for percutaneous nephrolithotomy: a cross-sectional study.

Authors:  Andrea Gallioli; Elisa De Lorenzis; Luca Boeri; Maurizio Delor; Stefano Paolo Zanetti; Fabrizio Longo; Alberto Trinchieri; Emanuele Montanari
Journal:  BMC Urol       Date:  2017-11-16       Impact factor: 2.264

8.  A new method for predicting uric acid composition in urinary stones using routine single-energy CT.

Authors:  Mats Lidén
Journal:  Urolithiasis       Date:  2017-06-28       Impact factor: 3.436

9.  Influence of a Deep Learning Noise Reduction on the CT Values, Image Noise and Characterization of Kidney and Ureter Stones.

Authors:  Andrea Steuwe; Birte Valentin; Oliver T Bethge; Alexandra Ljimani; Günter Niegisch; Gerald Antoch; Joel Aissa
Journal:  Diagnostics (Basel)       Date:  2022-07-05

10.  Stone heterogeneity index as the standard deviation of Hounsfield units: A novel predictor for shock-wave lithotripsy outcomes in ureter calculi.

Authors:  Joo Yong Lee; Jae Heon Kim; Dong Hyuk Kang; Doo Yong Chung; Dae Hun Lee; Hae Do Jung; Jong Kyou Kwon; Kang Su Cho
Journal:  Sci Rep       Date:  2016-04-01       Impact factor: 4.379

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.