Using machine learning algorithms to review computed tomography scans and assess risk for cardiovascular disease: Retrospective analysis from the National Lung Screening Trial (NLST)
Open accessAbstract
BACKGROUND: The National Lung Screening Trial (NLST) demonstrated that annual screening with low dose CT in high-risk population was associated with reduction in lung cancer mortality. Nonetheless, the leading cause of mortality in the study was from cardiovascular diseases. PURPOSE: To determine whether the used machine learning automatic algorithms assessing coronary calcium score (CCS), level of liver steatosis and emphysema percentage in the lungs are good predictors of cardiovascular disease (CVD) mortality and incidence when applied on low dose CT scans. MATERIALS AND METHODS: Three fully automated machine learning algorithms were used to assess CCS, level of liver steatosis and emphysema percentage in the lung. The algorithms were used on low-dose computed tomography scans acquired from 12,332 participants in NLST. RESULTS: In a multivariate analysis, association between the three algorithm scores and CVD mortality have shown an OR of 1.72 (p = 0.003), 2.62 (p < 0.0001) for CCS scores of 101-400 and above 400 respectively, and an OR of 1.12 (p = 0.044) for level of liver steatosis. Similar results were shown for the incidence of CVD, OR of 1.96 (p < 0.0001), 4.94 (p < 0.0001) for CCS scores of 101-400 and above 400 respectively. Also, emphysema percentage demonstrated an OR of 0.89 (p < 0.0001). Similar results are shown for univariate analyses of the algorithms. CONCLUSION: The three automated machine learning algorithms could help physicians to assess the incidence and risk of CVD mortality in this specific population. Application of these algorithms to existing LDCT scans can provide valuable health care information and assist in future research.
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Risk of cardiovascular death increased with CAC burden: CAC 101-400, OR 1.72; CAC >400, OR 2.62.
“Risk increased with CAC burden, for example, CVD for CAC 101–400, OR 1.72; CAC >400, OR 2.62.32”
From Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review (2026) · cites this paper as [32]
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- Authors
- Stemmer, A., Shadmi, R., Bregman-Amitai, O., Chettrit, D., Blagev, D., Orlovsky, M., Deutsch, L., & Elnekave, E.
- Affiliations
- Tel Aviv University (IL)
- University of Utah (US)
- Corresponding author
- Stemmer, Amos
- ORCID
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2020-08-03
- Volume
- 15
- Issue
- 8
- Pages
- e0236021
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC7398499
- OpenAlex
- W3047611709
- Cited by
- 27
- References
- 30
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 5 · 2023: 7 · 2022: 4 · 2021: 4
- FWCI
- 1.83
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Zebra Medical Vision
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
- Keywords
- cardiovascular risk, coronary artery calcium score, hepatic steatosis, emphysema, low-dose CT, automated machine learning, cardiovascular mortality, cardiovascular disease incidence, National Lung Screening Trial, lung cancer screening
- MeSH terms
- Machine Learning, Cigarette Smoking, Cardiovascular Diseases, Coronary Vessels, Emphysema, Fatty Liver, Female, Humans, Liver, Lung, Lung Neoplasms, Male, Mass Screening, Middle Aged, Radiographic Image Interpretation, Computer-Assisted, Retrospective Studies, Risk Factors, Tomography, X-Ray Computed, United States, Randomized Controlled Trials as Topic, Clinical Trials, Phase III as Topic, Risk Assessment, National Cancer Institute (U.S.), Early Detection of Cancer
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