Automated calcium scores collected during myocardial perfusion imaging improve identification of obstructive coronary artery disease
Open accessAbstract
BACKGROUND: Myocardial perfusion imaging (MPI) is an accurate noninvasive test for patients with suspected obstructive coronary artery disease (CAD) and coronary artery calcium (CAC) score is known to be a powerful predictor of cardiovascular events. Collection of CAC scores simultaneously with MPI is unexplored. AIM: We aimed to investigate whether automatically derived CAC scores during myocardial perfusion imaging would further improve the diagnostic accuracy of MPI to detect obstructive CAD. METHODS: We analyzed 150 consecutive patients without a history of coronary revascularization with suspected obstructive CAD who were referred for 82Rb PET/CT and available coronary angiographic data. Myocardial perfusion was evaluated both semi quantitatively as well as quantitatively according to the European guidelines. CAC scores were automatically derived from the low-dose attenuation correction CT scans using previously developed software based on deep learning. Obstructive CAD was defined as stenosis >70% (or >50% in the left main coronary artery) and/or fractional flow reserve (FFR) ≤0.80. RESULTS: In total 58% of patients had obstructive CAD of which seventy-four percent were male. Addition of CAC scores to MPI and clinical predictors significantly improved the diagnostic accuracy of MPI to detect obstructive CAD. The area under the curve (AUC) increased from 0.87 to 0.91 (p: 0.025). Sensitivity and specificity analysis showed an incremental decrease in false negative tests with our MPI + CAC approach (n = 14 to n = 4), as a consequence an increase in false positive tests was seen (n = 11 to n = 28). CONCLUSION: CAC scores collected simultaneously with MPI improve the detection of obstructive coronary artery disease in patients without a history of coronary revascularization.
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AI-CAC models generally relied on traditional categorical CAC (e.g., Agatston score risk categories of 0, 1-99, 100-399, ≥400) along with clinical features as core inputs, although several approaches extended quantification to individual coronary vessels or segments.
“AI-CAC models generally relied on traditional categorical CAC (eg, Agatston score risk categories of 0, 1–99, 100–399, ≥400) along with clinical features as core inputs, although several approaches extended quantification to the level of individual coronary vessels or segments to capture regional burden and distribution.3334”
From Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review (2026) · cites this paper as [33]
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Cites
Quantification of coronary artery calcium using ultrafast computed tomography
Agatston, A. S., Janowitz, W. R., Hildner, F. J., Zusmer, N. R., Viamonte, M., & Detrano, R. (1990) · Journal of the American College of Cardiology · doi:10.1016/0735-1097(90)90282-t · Cited by 7,786 · see citing works
Standardized Myocardial Segmentation and Nomenclature for Tomographic Imaging of the Heart
Cerqueira, M. D., Weissman, N. J., Dilsizian, V., Jacobs, A. K., Kaul, S., Laskey, W. K., Pennell, D. J., Rumberger, J. A., Ryan, T., & Verani, M. S. (2002) · Circulation · doi:10.1161/hc0402.102975 · Cited by 7,043 · see citing works
Coronary Calcium as a Predictor of Coronary Events in Four Racial or Ethnic Groups
Detrano, R., Guerci, A. D., Carr, J. J., Bild, D. E., Burke, G., Folsom, A. R., Liu, K., Shea, S., Szklo, M., Bluemke, D. A., et al. (2008) · New England Journal of Medicine · doi:10.1056/nejmoa072100 · Cited by 2,989 · see citing works
Greenland, P. (2004) · JAMA · doi:10.1001/jama.291.2.210 · Cited by 1,825 · see citing works
Guidelines on the management of stable angina pectoris: executive summary
Authors/Task Force Members, Fox, K. M., Garcia, M. A. A., Ardissino, D., Buszman, P. E., Camici, P. G., Crea, F., Daly, C., Backer, G. D., Hjemdahl, P., et al. (2006) · European Heart Journal · doi:10.1093/eurheartj/ehl001 · Cited by 1,324 · see citing works
Coronary Artery Calcium Score and Risk Classification for Coronary Heart Disease Prediction
Polonsky, T. S. (2010) · JAMA · doi:10.1001/jama.2010.461 · Cited by 1,123 · see citing works
Yeboah, J., McClelland, R. L., Polonsky, T. S., Burke, G. L., Sibley, C. T., O’Leary, D., Carr, J. J., Goff, D. C., Greenland, P., & Herrington, D. M. (2012) · JAMA · doi:10.1001/jama.2012.9624 · Cited by 1,122 · see citing works
Improved Cardiac Risk Assessment With Noninvasive Measures of Coronary Flow Reserve
Murthy, V. L., Naya, M., Foster, C. R., Hainer, J. M., Gaber, M., Carli, G. D., Blankstein, R., Dorbala, S., Sitek, A., Pencina, M. J., et al. (2011) · Circulation · doi:10.1161/circulationaha.111.050427 · Cited by 852 · see citing works
McClelland, R. L., Jorgensen, N. W., Budoff, M. J., Blaha, M. J., Post, W. S., Kronmal, R. A., Bild, D. E., Shea, S., Liu, K., Watson, K. E., et al. (2015) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2015.08.035 · Cited by 685 · see citing works
Long-Term Prognostic Value of 13N-Ammonia Myocardial Perfusion Positron Emission Tomography
Herzog, B. A., Husmann, L., Valenta, I., Gaemperli, O., Siegrist, P. T., Tay, F. M., Burkhard, N., Wyss, C. A., & Kaufmann, P. A. (2009) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2009.02.069 · Cited by 616 · see citing works
Ziadi, M. C., deKemp, R. A., Williams, K., Guo, A., Chow, B. J. W., Renaud, J. M., Ruddy, T. D., Sarveswaran, N., Tee, R., & Beanlands, R. S. (2011) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2011.01.065 · Cited by 606 · see citing works
Murthy, V. L., Naya, M., Foster, C. R., Gaber, M., Hainer, J. M., Klein, J., Dorbala, S., Blankstein, R., & Carli, M. F. D. (2012) · Circulation · doi:10.1161/circulationaha.112.120402 · Cited by 549 · see citing works
EANM/ESC procedural guidelines for myocardial perfusion imaging in nuclear cardiology
Hesse, B., Tägil, K., Cuocolo, A., Anagnostopoulos, C., Bardiès, M., Bax, J. J., Bengel, F. M., Sokole, E. B., Davies, G., Dondi, M., et al. (2005) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-005-1779-y · Cited by 526 · see citing works
Peters, S. A. E., den Ruijter, H. M., Bots, M. L., & Moons, K. G. M. (2011) · Heart · doi:10.1136/heartjnl-2011-300747 · Cited by 412 · see citing works
Taqueti, V. R., Shaw, L. J., Cook, N. R., Murthy, V. L., Shah, N. R., Foster, C. R., Hainer, J. M., Blankstein, R., Dorbala, S., & Carli, M. F. D. (2016) · Circulation · doi:10.1161/circulationaha.116.023266 · Cited by 300 · see citing works
Cerqueira, M. D., Weissman, N. J., Dilsizian, V., Jacobs, A. K., Kaul, S., Laskey, W., Pennell, D. J., Rumberger, J. A., Ryan, T., & Verani, M. S. (2002) · PubMed · Cited by 288 · see citing works
Sampson, U. K. A., Dorbala, S., Limaye, A. M., Kwong, R. Y. K., & Carli, M. F. D. (2007) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2006.12.015 · Cited by 275 · see citing works
Automatic Calcium Scoring in Low-Dose Chest CT Using Deep Neural Networks With Dilated Convolutions
Lessmann, N., van Ginneken, B., Zreik, M., de Jong, P. A., de Vos, B. D., Viergever, M. A., & Isgum, I. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2769839 · Cited by 263 · see citing works
Self-citationEinstein, A. J., Johnson, L. L., Bokhari, S., Son, J., Thompson, R. C., Bateman, T. M., Hayes, S. W., & Berman, D. S. (2010) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2010.05.057 · Cited by 235 · see citing works
Schepis, T., Gaemperli, O., Koepfli, P., Namdar, M., Valenta, I., Scheffel, H., Leschka, S., Husmann, L., Eberli, F. R., Lüscher, T. F., et al. (2007) · Journal of Nuclear Medicine · doi:10.2967/jnumed.107.040758 · Cited by 147 · see citing works
Physiological Basis for Angina and ST-Segment Change
Johnson, N. P., & Gould, K. L. (2011) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2011.06.015 · Cited by 131 · see citing works
Leschka, S., Scheffel, H., Desbiolles, L., Plass, A., Gaemperli, O., Stolzmann, P., Genoni, M., Lüscher, T. F., Marinček, B., Kaufmann, P. A., et al. (2007) · Heart · doi:10.1136/hrt.2007.124800 · Cited by 70 · see citing works
Zampella, E., Acampa, W., Assante, R., Nappi, C., Gaudieri, V., Mainolfi, C. G., Green, R., Cantoni, V., Panico, M., Klain, M., et al. (2018) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-018-3935-1 · Cited by 69 · see citing works
Automatic determination of cardiovascular risk by CT attenuation correction maps in Rb-82 PET/CT
Išgum, I., de Vos, B. D., Wolterink, J. M., Dey, D., Berman, D. S., Rubeaux, M., Leiner, T., & Slomka, P. J. (2017) · Journal of Nuclear Cardiology · doi:10.1007/s12350-017-0866-3 · Cited by 66 · see citing works
Self-citationBybee, K. A., Lee, J., Markiewicz, R., Longmore, R. B., McGhie, A. I., O’Keefe, J. H., Hsu, B., Kennedy, K. F., Thompson, R. C., & Bateman, T. M. (2009) · Journal of Nuclear Cardiology · doi:10.1007/s12350-009-9159-9 · Cited by 65 · see citing works
Clinical utility of coronary calcium scoring after nonischemic myocardial perfusion imaging
Thompson, R. C., McGhie, A. I., Moser, K. W., O’Keefe, J. H., Stevens, T. L., House, J. A., Fritsch, N., & Bateman, T. M. (2005) · Journal of Nuclear Cardiology · doi:10.1016/j.nuclcard.2005.04.006 · Cited by 64 · see citing works
Brodov, Y., Gransar, H., Dey, D., Shaleṿ, A., Germano, G., Friedman, J. D., Hayes, S. W., Thomson, L., Rogatko, A., Berman, D. S., et al. (2015) · Journal of Nuclear Medicine · doi:10.2967/jnumed.114.153429 · Cited by 59 · see citing works
Diagnosing coronary artery disease with hybrid PET/CT: It takes two to tango
Danad, I., Raijmakers, P. G. H. M., & Knaapen, P. (2013) · Journal of Nuclear Cardiology · doi:10.1007/s12350-013-9753-8 · Cited by 55 · see citing works
Mylonas, I., Kazmi, M. H., Fuller, L., deKemp, R. A., Yam, Y., Chen, L., Beanlands, R. S., & Chow, B. J. W. (2012) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jes079 · Cited by 54 · see citing works
Kaster, T. S., Dwivedi, G., Susser, L., Renaud, J. M., Beanlands, R. S., Chow, B. J. W., & deKemp, R. A. (2014) · Journal of Nuclear Cardiology · doi:10.1007/s12350-014-0026-y · Cited by 31 · see citing works
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Quer, G., Arnaout, R., Henne, M., & Arnaout, R. (2021) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2020.11.030 · Cited by 346 · see citing works
Slart, R. H. J. A., Williams, M. A., Juárez‐Orozco, L. E., Rischpler, C., Dweck, M. R., Glaudemans, A. W. J. M., Gimelli, A., Georgoulias, P., Gheysens, O. S., Gaemperli, O., et al. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-021-05341-z · Cited by 86 · see citing works
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Dekker, M., Waissi, F., Bank, I. E. M., Išgum, I., Scholtens, A. M., Velthuis, B. K., Pasterkamp, G., de Winter, R. J., Mosterd, A., Timmers, L., et al. (2021) · International Journal of Cardiology · doi:10.1016/j.ijcard.2020.12.079 · Cited by 20 · see citing works
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Self-citation
- Authors
- Dekker, M., Waissi, F., Bank, I. E. M., Lessmann, N., Išgum, I., Velthuis, B. K., Scholtens, A. M., Leenders, G. E., Pasterkamp, G., de Kleijn, D. P. V., Timmers, L., & Mosterd, A.
- Affiliations
- University Medical Center Utrecht (NL)
- St. Antonius Ziekenhuis (NL)
- Meander Medisch Centrum (NL)
- Corresponding author
- Dekker, Mirthe
- ORCID
- Venue
- IJC Heart & Vasculature
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 26
- Pages
- 100434
- ISSN
- 2352-9067 (ISSN-L)
- PMC
- PMC6872848
- OpenAlex
- W2991287954
- Cited by
- 20
- References
- 30
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 2 · 2023: 1 · 2022: 4 · 2021: 9 · 2020: 2
- FWCI
- 2.48
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IJC Heart & Vasculature (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Pure Amsterdam UMC (submittedVersion, other-oa)
- Funders
- Dutch Heart Foundation
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Health Sciences › Medicine › Surgery › Coronary Interventions and Diagnostics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- coronary artery calcium score, myocardial perfusion imaging, obstructive coronary artery disease, deep learning, diagnostic accuracy, fractional flow reserve, coronary artery stenosis
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