Literature DB >> 22771886

Interplatform reproducibility of CT coronary calcium scoring software.

Markus Weininger1, Kristin S Ritz, U Joseph Schoepf, Thomas G Flohr, Rozemarijn Vliegenthart, Philip Costello, Dietbert Hahn, Matthias Beissert.   

Abstract

PURPOSE: To investigate whether coronary artery calcium (CAC) scoring performed on three different workstations generates comparable and thus vendor-independent results.
MATERIALS AND METHODS: Institutional review board and Federal Office for Radiation Protection approval were received, as was each patient's written informed consent. Fifty-nine patients (37 men, 22 women; mean age, 57 years±3 [standard deviation]) underwent CAC scoring with use of 64-section multidetector computed tomography (CT) with retrospective electrocardiographic gating (one examination per patient). Data sets were created at 10% increments of the R-R interval from 40%-80%. Two experienced observers in consensus calculated Agatston and volume scores for all data sets by using the calcium scoring software of three different workstations. Comparative analysis of CAC scores between the workstations was performed by using regression analysis, Spearman rank correlation (rs), and the Kruskal-Wallis test.
RESULTS: Each workstation produced different absolute numeric results for Agatston and volume scores. However, statistical analysis revealed excellent correlation between the workstations, with highest correlation at 60% of the R-R interval (minimal rs=0.998; maximal rs=0.999) for both scoring methods. No significant differences were detected for Agatston and volume score results between the software platforms. At analysis of individual reconstruction intervals, each workstation demonstrated the same score variability, with the consequence that 12 of 59 patients were assigned to divergent cardiac risk groups by using at least one of the workstations.
CONCLUSION: While mere numeric values might be different, commercially available software platforms produce comparable CAC scoring results, which suggests a vendor-independence of the method; however, none of the analyzed software platforms appears to provide a distinct advantage for risk stratification, as the variability of CAC scores depending on the reconstruction interval persists across platforms. © RSNA, 2012.

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Year:  2012        PMID: 22771886     DOI: 10.1148/radiol.12112532

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  8 in total

1.  Influence of heart rate on coronary calcium scores: a multi-manufacturer phantom study.

Authors:  N R van der Werf; M J Willemink; T P Willems; R Vliegenthart; M J W Greuter; T Leiner
Journal:  Int J Cardiovasc Imaging       Date:  2017-12-28       Impact factor: 2.357

2.  Quantification of Aortic Valve Calcifications Detected During Lung Cancer-Screening CT Helps Stratify Subjects Necessitating Echocardiography for Aortic Stenosis Diagnosis.

Authors:  Hee Young Lee; Sung Mok Kim; Kyung Soo Lee; Seung Woo Park; Myung Jin Chung; Hyoun Cho; Jung Im Jung; Hye Won Jang; Sin-Ho Jung; Juna Goo
Journal:  Medicine (Baltimore)       Date:  2016-05       Impact factor: 1.889

3.  Reproducibility of calcium scoring of the coronary arteries: comparison between different vendors and iterative reconstructions.

Authors:  Kyu Sung Choi; Whal Lee; Joon Hyung Jung; Eun-Ah Park
Journal:  Acta Radiol Open       Date:  2020-04-28

4.  Evaluation of an AI-based, automatic coronary artery calcium scoring software.

Authors:  Mårten Sandstedt; Lilian Henriksson; Magnus Janzon; Gusten Nyberg; Jan Engvall; Jakob De Geer; Joakim Alfredsson; Anders Persson
Journal:  Eur Radiol       Date:  2019-11-14       Impact factor: 5.315

5.  End-to-End, Pixel-Wise Vessel-Specific Coronary and Aortic Calcium Detection and Scoring Using Deep Learning.

Authors:  Gurpreet Singh; Subhi J Al'Aref; Benjamin C Lee; Jing Kai Lee; Swee Yaw Tan; Fay Y Lin; Hyuk-Jae Chang; Leslee J Shaw; Lohendran Baskaran
Journal:  Diagnostics (Basel)       Date:  2021-02-02

6.  Thoracic Aorta Calcium Detection and Quantification Using Convolutional Neural Networks in a Large Cohort of Intermediate-Risk Patients.

Authors:  Federico N Guilenea; Mariano E Casciaro; Ariel F Pascaner; Gilles Soulat; Elie Mousseaux; Damian Craiem
Journal:  Tomography       Date:  2021-10-28

7.  Feasibility of spectral shaping for detection and quantification of coronary calcifications in ultra-low dose CT.

Authors:  Marleen Vonder; Gert Jan Pelgrim; Sèvrin E M Huijsse; Mathias Meyer; Marcel J W Greuter; Thomas Henzler; Thomas G Flohr; Matthijs Oudkerk; Rozemarijn Vliegenthart
Journal:  Eur Radiol       Date:  2016-08-29       Impact factor: 5.315

8.  Quantification of abdominal aortic calcification: Inherent measurement errors in current computed tomography imaging.

Authors:  Ruben V C Buijs; Eva L Leemans; Marcel Greuter; Ignace F J Tielliu; Clark J Zeebregts; Tineke P Willems
Journal:  PLoS One       Date:  2018-02-28       Impact factor: 3.240

  8 in total

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