European Radiology

, Volume 20, Issue 5, pp 1160–1167 | Cite as

Automated computer-aided stenosis detection at coronary CT angiography: initial experience

  • Elisabeth Arnoldi
  • Mulugeta Gebregziabher
  • U. Joseph Schoepf
  • Roman Goldenberg
  • Luis Ramos-Duran
  • Peter L. Zwerner
  • Konstantin Nikolaou
  • Maximilian F. Reiser
  • Philip Costello
  • Christian Thilo
Cardiac

Abstract

Objective

To evaluate the performance of a computer-aided algorithm for automated stenosis detection at coronary CT angiography (cCTA).

Methods

We investigated 59 patients (38 men, mean age 58 ± 12 years) who underwent cCTA and quantitative coronary angiography (QCA). All cCTA data sets were analyzed using a software algorithm for automated, without human interaction, detection of coronary artery stenosis. The performance of the algorithm for detection of stenosis of 50% or more was compared with QCA.

Results

QCA revealed a total of 38 stenoses of 50% or more of which the algorithm correctly identified 28 (74%). Overall, the automated detection algorithm had 74%/100% sensitivity, 83%/65% specificity, 46%/58% positive predictive value, and 94%/100% negative predictive value for diagnosing stenosis of 50% or more on per-vessel/per-patient analysis, respectively. There were 33 false positive detection marks (average 0.56/patient), of which 19 were associated with stenotic lesions of less than 50% on QCA and 14 were not associated with an atherosclerotic surrogate.

Conclusion

Compared with QCA, the automated detection algorithm evaluated has relatively high accuracy for diagnosing significant coronary artery stenosis at cCTA. If used as a second reader, the high negative predictive value may further enhance the confidence of excluding significant stenosis based on a normal or near-normal cCTA study.

Keywords

Coronary artery disease Coronary artery stenosis Computed tomography Computer-aided detection Computer-aided diagnosis 

Notes

Conflict of Interest

UJS is a medical consultant for and receives research support from Bayer-Schering, Bracco, General Electric, Medrad, and Siemens. RG is an employee of Rcadia Medical Imaging Ltd. PLZ receives research support from Boehringer-Ingelheim, Bristol Myers Squib, Bracco, and Siemens. PC is a medical consultant for Bracco and receives research support from Siemens.

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Copyright information

© European Society of Radiology 2009

Authors and Affiliations

  • Elisabeth Arnoldi
    • 1
    • 2
  • Mulugeta Gebregziabher
    • 3
  • U. Joseph Schoepf
    • 1
    • 4
  • Roman Goldenberg
    • 5
  • Luis Ramos-Duran
    • 1
  • Peter L. Zwerner
    • 1
    • 4
  • Konstantin Nikolaou
    • 2
  • Maximilian F. Reiser
    • 2
  • Philip Costello
    • 1
  • Christian Thilo
    • 1
    • 4
    • 6
  1. 1.Department of Radiology and Radiological ScienceMedical University of South CarolinaCharlestonUSA
  2. 2.Department of Clinical RadiologyUniversity Hospitals Munich–Grosshadern Campus, Ludwig-Maximilians UniversityMunichGermany
  3. 3.Department of Biostatistics, Bioinformatics and EpidemiologyMedical University of South CarolinaCharlestonUSA
  4. 4.Department of Medicine, Division of CardiologyMedical University of South CarolinaCharlestonUSA
  5. 5.Department of Research and DevelopmentRcadia Medical Imaging Ltd.HaifaIsrael
  6. 6.Department of CardiologyKlinikum Augsburg, Herzzentrum Augsburg-SchwabenAugsburgGermany

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