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Driessen RS, Raijmakers PG, Danad I, Stuijfzand WJ, Schumacher SP, Leipsic JA, Min JK, Knuuti J, Lammertsma AA, van Rossum AC, van Royen N, Underwood SR, Knaapen P. Automated SPECT analysis compared with expert visual scoring for the detection of FFR-defined coronary artery disease. Eur J Nucl Med Mol Imaging 2018; 45:1091-1100. [PMID: 29470616 PMCID: PMC5954003 DOI: 10.1007/s00259-018-3951-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Accepted: 01/16/2018] [Indexed: 01/01/2023]
Abstract
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 99mTc-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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Affiliation(s)
- R S Driessen
- Department of Cardiology, VU University Medical Center, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - P G Raijmakers
- Department of Radiology & Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - I Danad
- Department of Cardiology, VU University Medical Center, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - W J Stuijfzand
- Department of Cardiology, VU University Medical Center, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - S P Schumacher
- Department of Cardiology, VU University Medical Center, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - J A Leipsic
- Department of Radiology, St. Paul's Hospital, Vancouver, Canada
| | - J K Min
- Department of Radiology, Weill Cornell Medical College, New York-Presbyterian Hospital, New York, USA
| | - J Knuuti
- Turku University Hospital and University of Turku, Turku, Finland
| | - A A Lammertsma
- Department of Radiology & Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - A C van Rossum
- Department of Cardiology, VU University Medical Center, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - N van Royen
- Department of Cardiology, VU University Medical Center, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - S R Underwood
- Department of Nuclear Medicine, Royal Brompton Hospital, London, UK
| | - P Knaapen
- Department of Cardiology, VU University Medical Center, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.
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Abstract
PURPOSE OF REVIEW Myocardial perfusion imaging (MPI) with SPECT is performed clinically worldwide to detect and monitor coronary artery disease (CAD). MPI allows an objective quantification of myocardial perfusion at stress and rest. This established technique relies on normal databases to compare patient scans against reference normal limits. In this review, we aim to introduce the process of MPI quantification with normal databases and describe the associated perfusion quantitative measures that are used. RECENT FINDINGS New equipment and new software reconstruction algorithms have been introduced which require the development of new normal limits. The appearance and regional count variations of normal MPI scan may differ between these new scanners and standard Anger cameras. Therefore, these new systems may require the determination of new normal limits to achieve optimal accuracy in relative myocardial perfusion quantification. Accurate diagnostic and prognostic results rivaling those obtained by expert readers can be obtained by this widely used technique. SUMMARY Throughout this review, we emphasize the importance of the different normal databases and the need for specific databases relative to distinct imaging procedures. use of appropriate normal limits allows optimal quantification of MPI by taking into account subtle image differences due to the hardware and software used, and the population studied.
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