Document Detail


Accuracy of automated software-guided detection of significant coronary artery stenosis by CT angiography: comparison with invasive catheterisation.
MedLine Citation:
PMID:  23207868     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
PURPOSE: True automated detection of coronary artery stenoses might be useful whenever expert evaluation is not available, or as a "second reader" to enhance diagnostic confidence. We evaluated the accuracy of a PC-based stenosis detection tool alone and combined with expert interpretation.
METHODS: One hundred coronary CT angiography datasets were evaluated with the automated software alone, by manual interpretation (axial images, multiplanar reformations and maximum intensity projections in free double-oblique planes), and by expert interpretation aware of the automated findings. Stenoses ≥ 50 % were noted per-vessel and per-patient, and compared with invasive angiography.
RESULTS: Automated post-processing was successful in 90 % of patients (88 % of vessels). When excluding uninterpretable datasets, per-patient sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 89 %, 79 %, 74 % and 92 % (per-vessel: 82 %, 85 %, 48 % and 96 %). All 100 datasets were evaluable by expert interpretation. Per-patient sensitivity, specificity, PPV and NPV were 95 %, 95 %, 93 % and 97 % (per-vessel: 89 %,98 %, 88 % and 98 %). Knowing the results of automated interpretation did not improve the performance of expert readers.
CONCLUSION: Automated off-line post-processing of coronary CT angiography shows adequate sensitivity, but relatively low specificity in coronary stenosis detection. It does not increase accuracy of expert interpretation. Failure of post-processing in 10 % of all patients necessitates additional manual image work-up.
KEY POINTS: • Coronary CT angiography is increasingly used for detection of coronary artery stenosis • Computer assisted diagnosis might facilitate and speed up interpretation • Performance in properly segmented cases compared favourably with manual image interpretation • However, automated segmentation failed in about 10 % of cases • Manual reading is still mandatory; computer assisted diagnosis can provide a useful second read.
Authors:
Katharina Anders; Stephan Achenbach; Isabel Petit; Werner G Daniel; Michael Uder; Tobias Pflederer
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Publication Detail:
Type:  Comparative Study; Journal Article     Date:  2012-12-04
Journal Detail:
Title:  European radiology     Volume:  23     ISSN:  1432-1084     ISO Abbreviation:  Eur Radiol     Publication Date:  2013 May 
Date Detail:
Created Date:  2013-04-11     Completed Date:  2013-09-30     Revised Date:  2013-11-07    
Medline Journal Info:
Nlm Unique ID:  9114774     Medline TA:  Eur Radiol     Country:  Germany    
Other Details:
Languages:  eng     Pagination:  1218-25     Citation Subset:  IM    
Affiliation:
Department of Radiology, University of Erlangen, Maximiliansplatz 1, 91054 Erlangen, Germany. katharina.anders@uk-erlangen.de
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MeSH Terms
Descriptor/Qualifier:
Adult
Aged
Algorithms
Cardiac Catheterization / methods*
Coronary Angiography / methods*
Coronary Stenosis / radiography*
Female
Humans
Male
Middle Aged
Observer Variation
Pattern Recognition, Automated / methods*
Radiographic Image Enhancement / methods
Radiographic Image Interpretation, Computer-Assisted / methods*
Reproducibility of Results
Sensitivity and Specificity
Software*
Software Validation
Tomography, X-Ray Computed / methods*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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