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Automated SPECT analysis compared with expert visual scoring for the detection of FFR-defined coronary artery disease

PURPOSE: Traditionally, interpretation of myocardial perfusion imaging (MPI) is based on visual assessment. Computer-based automated analysis might be a simple alternative obviating the need for extensive reading experience. Therefore, the aim of the present study was to compare the diagnostic perfo...

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Autores principales: Driessen, R. S., Raijmakers, P. G., Danad, I., Stuijfzand, W. J., Schumacher, S. P., Leipsic, J.A., Min, J. K., Knuuti, J., Lammertsma, A. A., van Rossum, A. C., van Royen, N., Underwood, S. R., Knaapen, P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5954003/
https://www.ncbi.nlm.nih.gov/pubmed/29470616
http://dx.doi.org/10.1007/s00259-018-3951-1
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author Driessen, R. S.
Raijmakers, P. G.
Danad, I.
Stuijfzand, W. J.
Schumacher, S. P.
Leipsic, J.A.
Min, J. K.
Knuuti, J.
Lammertsma, A. A.
van Rossum, A. C.
van Royen, N.
Underwood, S. R.
Knaapen, P.
author_facet Driessen, R. S.
Raijmakers, P. G.
Danad, I.
Stuijfzand, W. J.
Schumacher, S. P.
Leipsic, J.A.
Min, J. K.
Knuuti, J.
Lammertsma, A. A.
van Rossum, A. C.
van Royen, N.
Underwood, S. R.
Knaapen, P.
author_sort Driessen, R. S.
collection PubMed
description PURPOSE: Traditionally, interpretation of myocardial perfusion imaging (MPI) is based on visual assessment. Computer-based automated analysis might be a simple alternative obviating the need for extensive reading experience. Therefore, the aim of the present study was to compare the diagnostic performance of automated analysis with that of expert visual reading for the detection of obstructive coronary artery disease (CAD). METHODS: 206 Patients (64% men, age 58.2 ± 8.7 years) with suspected CAD were included prospectively. All patients underwent (99m)Tc-tetrofosmin single-photon emission computed tomography (SPECT) and invasive coronary angiography with fractional flow reserve (FFR) measurements. Non-corrected (NC) and attenuation-corrected (AC) SPECT images were analyzed both visually as well as automatically by commercially available SPECT software. Automated analysis comprised a segmental summed stress score (SSS), summed difference score (SDS), stress total perfusion deficit (S-TPD), and ischemic total perfusion deficit (I-TPD), representing the extent and severity of hypoperfused myocardium. Subsequently, software was optimized with an institutional normal database and thresholds. Diagnostic performances of automated and visual analysis were compared taking FFR as a reference. RESULTS: Sensitivity did not differ significantly between visual reading and most automated scoring parameters, except for SDS, which was significantly higher than visual assessment (p < 0.001). Specificity, however, was significantly higher for visual reading than for any of the automated scores (p < 0.001 for all). Diagnostic accuracy was significantly higher for visual scoring (77.2%) than for all NC images scores (p < 0.05), but not compared with SSS AC and S-TPD AC (69.8% and 71.2%, p = 0.063 and p = 0.134). After optimization of the automated software, diagnostic accuracies were similar for visual (73.8%) and automated analysis. Among the automated parameters, S-TPD AC showed the highest accuracy (73.5%). CONCLUSION: Automated analysis of myocardial perfusion SPECT can be as accurate as visual interpretation by an expert reader in detecting significant CAD defined by FFR. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00259-018-3951-1) contains supplementary material, which is available to authorized users.
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spelling pubmed-59540032018-05-18 Automated SPECT analysis compared with expert visual scoring for the detection of FFR-defined coronary artery disease Driessen, R. S. Raijmakers, P. G. Danad, I. Stuijfzand, W. J. Schumacher, S. P. Leipsic, J.A. Min, J. K. Knuuti, J. Lammertsma, A. A. van Rossum, A. C. van Royen, N. Underwood, S. R. Knaapen, P. Eur J Nucl Med Mol Imaging Original Article PURPOSE: Traditionally, interpretation of myocardial perfusion imaging (MPI) is based on visual assessment. Computer-based automated analysis might be a simple alternative obviating the need for extensive reading experience. Therefore, the aim of the present study was to compare the diagnostic performance of automated analysis with that of expert visual reading for the detection of obstructive coronary artery disease (CAD). METHODS: 206 Patients (64% men, age 58.2 ± 8.7 years) with suspected CAD were included prospectively. All patients underwent (99m)Tc-tetrofosmin single-photon emission computed tomography (SPECT) and invasive coronary angiography with fractional flow reserve (FFR) measurements. Non-corrected (NC) and attenuation-corrected (AC) SPECT images were analyzed both visually as well as automatically by commercially available SPECT software. Automated analysis comprised a segmental summed stress score (SSS), summed difference score (SDS), stress total perfusion deficit (S-TPD), and ischemic total perfusion deficit (I-TPD), representing the extent and severity of hypoperfused myocardium. Subsequently, software was optimized with an institutional normal database and thresholds. Diagnostic performances of automated and visual analysis were compared taking FFR as a reference. RESULTS: Sensitivity did not differ significantly between visual reading and most automated scoring parameters, except for SDS, which was significantly higher than visual assessment (p < 0.001). Specificity, however, was significantly higher for visual reading than for any of the automated scores (p < 0.001 for all). Diagnostic accuracy was significantly higher for visual scoring (77.2%) than for all NC images scores (p < 0.05), but not compared with SSS AC and S-TPD AC (69.8% and 71.2%, p = 0.063 and p = 0.134). After optimization of the automated software, diagnostic accuracies were similar for visual (73.8%) and automated analysis. Among the automated parameters, S-TPD AC showed the highest accuracy (73.5%). CONCLUSION: Automated analysis of myocardial perfusion SPECT can be as accurate as visual interpretation by an expert reader in detecting significant CAD defined by FFR. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00259-018-3951-1) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2018-02-22 2018 /pmc/articles/PMC5954003/ /pubmed/29470616 http://dx.doi.org/10.1007/s00259-018-3951-1 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Original Article
Driessen, R. S.
Raijmakers, P. G.
Danad, I.
Stuijfzand, W. J.
Schumacher, S. P.
Leipsic, J.A.
Min, J. K.
Knuuti, J.
Lammertsma, A. A.
van Rossum, A. C.
van Royen, N.
Underwood, S. R.
Knaapen, P.
Automated SPECT analysis compared with expert visual scoring for the detection of FFR-defined coronary artery disease
title Automated SPECT analysis compared with expert visual scoring for the detection of FFR-defined coronary artery disease
title_full Automated SPECT analysis compared with expert visual scoring for the detection of FFR-defined coronary artery disease
title_fullStr Automated SPECT analysis compared with expert visual scoring for the detection of FFR-defined coronary artery disease
title_full_unstemmed Automated SPECT analysis compared with expert visual scoring for the detection of FFR-defined coronary artery disease
title_short Automated SPECT analysis compared with expert visual scoring for the detection of FFR-defined coronary artery disease
title_sort automated spect analysis compared with expert visual scoring for the detection of ffr-defined coronary artery disease
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5954003/
https://www.ncbi.nlm.nih.gov/pubmed/29470616
http://dx.doi.org/10.1007/s00259-018-3951-1
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