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Deep Learning for Automatic Calcium Scoring in CT: Validation Using Multiple Cardiac CT and Chest CT Protocols
Open accessAbstract
Background Although several deep learning (DL) calcium scoring methods have achieved excellent performance for specific CT protocols, their performance in a range of CT examination types is unknown. Purpose To evaluate the performance of a DL method for automatic calcium scoring across a wide range of CT examination types and to investigate whether the method can adapt to different types of CT examinations when representative images are added to the existing training data set. Materials and Methods The study included 7240 participants who underwent various types of nonenhanced CT examinations that included the heart: coronary artery calcium (CAC) scoring CT, diagnostic CT of the chest, PET attenuation correction CT, radiation therapy treatment planning CT, CAC screening CT, and low-dose CT of the chest. CAC and thoracic aorta calcification (TAC) were quantified using a convolutional neural network trained with (a) 1181 low-dose chest CT examinations (baseline), (b) a small set of examinations of the respective type supplemented to the baseline (data specific), and (c) a combination of examinations of all available types (combined). Supplemental training sets contained 199–568 CT images depending on the calcium burden of each population. The DL algorithm performance was evaluated with intraclass correlation coefficients (ICCs) between DL and manual (Agatston) CAC and (volume) TAC scoring and with linearly weighted κ values for cardiovascular risk categories (Agatston score; cardiovascular disease risk categories: 0, 1–10, 11–100, 101–400, >400). Results At baseline, the DL algorithm yielded ICCs of 0.79–0.97 for CAC and 0.66–0.98 for TAC across the range of different types of CT examinations. ICCs improved to 0.84–0.99 (CAC) and 0.92–0.99 (TAC) for CT protocol–specific training and to 0.85–0.99 (CAC) and 0.96–0.99 (TAC) for combined training. For assignment of cardiovascular disease risk category, the κ value for all test CT scans was 0.90 (95% confidence interval [CI]: 0.89, 0.91) for the baseline training. It increased to 0.92 (95% CI: 0.91, 0.93) for both data-specific and combined training. Conclusion A deep learning calcium scoring algorithm for quantification of coronary and thoracic calcium was robust, despite substantial differences in CT protocol and variations in subject population. Augmenting the algorithm training with CT protocol–specific images further improved algorithm performance. © RSNA, 2020 See also the editorial by Vannier in this issue.
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“A combined solution capable of analyzing cardiac and non-gated chest CT as presented in our investigation has only been proposed by de Vos et al.36 and van Velzen et al.41.”
From Deep convolutional neural networks to predict cardiovascular risk from computed tomography (2021) · cites this paper as [41]
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“Although other studies have investigated deep learning algorithms for automated coronary calcium quantification34–42, they used smaller cohorts or proprietary technologies.”
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Metadata
- Authors
- van Velzen, S. G. M., Lessmann, N., Velthuis, B. K., Bank, I. E. M., van den Bongard, D. H. J. G., Leiner, T., de Jong, P. A., Veldhuis, W. B., Correa, A., Terry, J. G., Carr, J. J., Viergever, M. A., Verkooijen, H. M., & Išgum, I.
- Affiliations
- Radboud University Medical Center (NL)
- ORCID
- Venue
- Radiology
- Published
- 2020-04
- Volume
- 295
- Issue
- 1
- Pages
- 66-79
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- PMC
- PMC7106943
- OpenAlex
- W3005949594
- Cited by
- 244
- References
- 34
- Citations by year
- 2026: 29 · 2025: 26 · 2024: 40 · 2023: 36 · 2022: 48 · 2021: 47 · 2020: 18
- FWCI
- 18.14
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Radiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (publishedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Radboud Repository (Radboud University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Funders
- U.S. Department of Health and Human Services (HHSN268201800014I, HHSN268201800010I, HHSN268201800013I, HHSN268201800012I, HHSN268201800015I)
- Jackson State University (HHSN268201800015I/HHSN26800001, HHSN268201800013 I, HHSN268201800014 I)
- Mississippi State Department of Health (HHSN26800001, HHSN268201800015 I, HHSN268201800015I/HHSN26800001)
- National Institutes of Health (HHSN268201800010I, HHSN268201800012I, HHSN268201800014I, HHSN26800001, HHSN268201800013I, HHSN268201800015I)
- National Heart, Lung, and Blood Institute (HHSN268201800013I, HHSN26800001, HHSN268201800012C, HHSN268201800010 I, HHSN268201800014 I, HHSN268201800012 I, HHSN268201100011I, HHSN268201800014C, HHSN268201800015I, HHSN268201800011C, HHSN268201800011I)
- National Cancer Institute (HHSN261201800014I, HHSN261201800012I, HHSN261201800010I)
- National Institute on Minority Health and Health Disparities (HHSN268201800012I, HHSN268201800010I, HHSN268201800014I, HHSN268201800015I, HHSN268201800013I)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- coronary artery calcium scoring, thoracic aortic calcification, deep learning, convolutional neural networks, chest CT, low-dose CT, cardiovascular risk stratification, Agatston score, cardiovascular risk prediction, cardiovascular risk prediction models
- MeSH terms
- Deep Learning, Aged, Clinical Protocols, Coronary Artery Disease, Female, Heart, Humans, Male, Middle Aged, Retrospective Studies, Thorax, Tomography, X-Ray Computed, Vascular Calcification
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