ArticlePloS one2020
Using machine learning algorithms to review computed tomography scans and assess risk for cardiovascular disease: Retrospective analysis from the National Lung Screening Trial (NLST).
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.
- Diagnostic performance of machine-learning algorithms for sepsis prediction: An updated meta-analysis.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Pooled it
- Lung cancer screening with AI can discover cures for many early diseases. A public utility can make sure it happens.Frontiers in oncology · 2026Article
- Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review.BMJ digital health & AI · 2026Article
- Non-Contrast Radiation-Free NIR Lung Imaging.Biomedicines · 2025Article
- Structured narrative review on lung cancer screening: current evidence, clinical practice implications and implementation insights from a multidisciplinary task force and patient representatives.European respiratory review : an official journal of the European Respiratory Society · 2025Review
- Pros and cons of reporting incidental findings in lung cancer screening.European radiology · 2025Review
- Applications, challenges and future directions of artificial intelligence in cardio-oncology.European journal of clinical investigation · 2025Review
- Artificial Intelligence Applications in Cardio-Oncology: A Comprehensive Review.Current cardiology reports · 2025Review
- AI for Multistructure Incidental Findings and Mortality Prediction at Chest CT in Lung Cancer Screening.Radiology · 2024Article
- Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging.Diagnostics (Basel, Switzerland) · 2024Review
- ERS/ESTS/ESTRO/ESR/ESTI/EFOMP statement on management of incidental findings from low dose CT screening for lung cancer.European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery · 2023Article
- Machine Learning in Cardio-Oncology: New Insights from an Emerging Discipline.Reviews in cardiovascular medicine · 2023Review
- Fully automated calcium scoring predicts all-cause mortality at 12 years in the MILD lung cancer screening trial.PloS one · 2023Article
- Computational clustering reveals differentiated coronary artery calcium progression at prevalent levels of pulse wave velocity by classifying high-risk patients.Frontiers in cardiovascular medicine · 2023Article
- Value-added Opportunistic CT Screening: State of the Art.Radiology · 2022Review
- Automated CT-Based Body Composition Analysis: A Golden Opportunity.Korean journal of radiology · 2021Article
- Distinctive Oculomotor Behaviors in Alzheimer's Disease and Frontotemporal Dementia.Frontiers in aging neuroscience · 2020Article
- Opportunistic Screening at Abdominal CT: Use of Automated Body Composition Biomarkers for Added Cardiometabolic Value.Radiographics : a review publication of the Radiological Society of North America, IncReview
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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
methodsThree 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.
resultsIn 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.
conclusionThe 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.