Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography

Chao, Hanqing; Shan, Hongming; Homayounieh, Fatemeh; Singh, Ramandeep; Khera, Ruhani Doda; Guo, Hengtao; Su, Timothy A.; Wang, Ge; Kalra, Mannudeep K.; Yan, Pingkun

doi:10.1038/s41467-021-23235-4

Cited by 59 publications

(53 citation statements)

References 51 publications

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“…Notably, the application of automated DL methods to routine clinical imaging for cardiovascular risk prediction has been demonstrated from images such as retinal fundus photographs and lung cancer screening CT scans. 23,[30][31][32] However, the use of routine oncologic imaging, such as radiotherapy planning CT scans, for cardiovascular risk prediction has been less well characterized. Importantly, DL studies analyzing CAC from various cardiac CT and chest CT protocols have demonstrated success, 33 including DL models specifically measuring CAC from breast cancer radiotherapy CT scans.…”

Section: Discussionmentioning

confidence: 99%

Elevated Coronary Artery Calcium Quantified by a Validated Deep Learning Model From Lung Cancer Radiotherapy Planning Scans Predicts Mortality

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Zeleznik

et al. 2022

JCO Clinical Cancer Informatics

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PURPOSE Coronary artery calcium (CAC) quantified on computed tomography (CT) scans is a robust predictor of atherosclerotic coronary disease; however, the feasibility and relevance of quantitating CAC from lung cancer radiotherapy planning CT scans is unknown. We used a previously validated deep learning (DL) model to assess whether CAC is a predictor of all-cause mortality and major adverse cardiac events (MACEs). METHODS Retrospective analysis of non–contrast-enhanced radiotherapy planning CT scans from 428 patients with locally advanced lung cancer is performed. The DL-CAC algorithm was previously trained on 1,636 cardiac-gated CT scans and tested on four clinical trial cohorts. Plaques ≥ 1 cubic millimeter were measured to generate an Agatston-like DL-CAC score and grouped as DL-CAC = 0 (very low risk) and DL-CAC ≥ 1 (elevated risk). Cox and Fine and Gray regressions were adjusted for lung cancer and cardiovascular factors. RESULTS The median follow-up was 18.1 months. The majority (61.4%) had a DL-CAC ≥ 1. There was an increased risk of all-cause mortality with DL-CAC ≥ 1 versus DL-CAC = 0 (adjusted hazard ratio, 1.51; 95% CI, 1.01 to 2.26; P = .04), with 2-year estimates of 56.2% versus 45.4%, respectively. There was a trend toward increased risk of major adverse cardiac events with DL-CAC ≥ 1 versus DL-CAC = 0 (hazard ratio, 1.80; 95% CI, 0.87 to 3.74; P = .11), with 2-year estimates of 7.3% versus 1.2%, respectively. CONCLUSION In this proof-of-concept study, CAC was effectively measured from routinely acquired radiotherapy planning CT scans using an automated model. Elevated CAC, as predicted by the DL model, was associated with an increased risk of mortality, suggesting a potential benefit for automated cardiac risk screening before cancer therapy begins.

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Section: Discussionmentioning

confidence: 99%

Elevated Coronary Artery Calcium Quantified by a Validated Deep Learning Model From Lung Cancer Radiotherapy Planning Scans Predicts Mortality

Atkins

Weiß

Zeleznik

et al. 2022

JCO Clinical Cancer Informatics

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“…Existing machine learning methods usually rely on generalised adverse features for CAD risk prediction which may lead to low reproducibility. 68 Additionally, current machine learning approaches, 37–41 71 72 focus primarily on systemic risk factors. This does not consider the observed trends that particular locations within the coronary tree, for example, bifurcations, 10 are at significantly higher risk of disease.…”

Section: Discussionmentioning

confidence: 99%

“…Still, cardiac CT requires unfavourable radiation exposure and some studies attempted to leverage non-cardiac imaging to investigate CAD risk factors. 37–39 Deep learning models have shown promising results in using low-dose CT imaging for lung cancer screening, 37 and risk factors such as blood pressure, smoking history and diabetes, have been successfully identified in retinal vasculature from retinal images only, 38 showing correlation with CAD risk and all-cause mortality. 39 This showcases the potential for general investigation of the anatomy of risk and patient-specific image-derived biomarkers, as these may not just be linked to cardiac CT but can also be deployed to a range of available imaging modalities.…”

Section: Introductionmentioning

confidence: 99%

A new and automated risk prediction of coronary artery disease using clinical endpoints and medical imaging-derived patient-specific insights: protocol for the retrospective GeoCAD cohort study

et al. 2022

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IntroductionCoronary artery disease (CAD) is the leading cause of death worldwide. More than a quarter of cardiovascular events are unexplained by current absolute cardiovascular disease risk calculators, and individuals without clinical risk factors have been shown to have worse outcomes. The ‘anatomy of risk’ hypothesis recognises that adverse anatomical features of coronary arteries enhance atherogenic haemodynamics, which in turn mediate the localisation and progression of plaques. We propose a new risk prediction method predicated on CT coronary angiography (CTCA) data and state-of-the-art machine learning methods based on a better understanding of anatomical risk for CAD. This may open new pathways in the early implementation of personalised preventive therapies in susceptible individuals as a potential key in addressing the growing burden of CAD.Methods and analysisGeoCAD is a retrospective cohort study in 1000 adult patients who have undergone CTCA for investigation of suspected CAD. It is a proof-of-concept study to test the hypothesis that advanced image-derived patient-specific data can accurately predict long-term cardiovascular events. The objectives are to (1) profile CTCA images with respect to variations in anatomical shape and associated haemodynamic risk expressing, at least in part, an individual’s CAD risk, (2) develop a machine-learning algorithm for the rapid assessment of anatomical risk directly from unprocessed CTCA images and (3) to build a novel CAD risk model combining traditional risk factors with these novel anatomical biomarkers to provide a higher accuracy CAD risk prediction tool.Ethics and disseminationThe study protocol has been approved by the St Vincent’s Hospital Human Research Ethics Committee, Sydney—2020/ETH02127 and the NSW Population and Health Service Research Ethics Committee—2021/ETH00990. The project outcomes will be published in peer-reviewed and biomedical journals, scientific conferences and as a higher degree research thesis.

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“…Multiple ML techniques have been proposed to automatically evaluate CAC score from dedicated cardiac and non-EKG gated chest CT scans [ 66 , 67 , 183 , 184 , 185 ]. ML techniques incorporating CAC score and other imaging parameters have been shown to be a better predictor than the traditional risk scores employed for cardiovascular disease risk stratification [ 181 , 186 , 187 , 188 , 189 ].…”

Section: Artificial Intelligence-based Long-term Mortality and Mace P...mentioning

confidence: 99%

Current and Future Applications of Artificial Intelligence in Coronary Artery Disease

et al. 2022

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Cardiovascular diseases (CVDs) carry significant morbidity and mortality and are associated with substantial economic burden on healthcare systems around the world. Coronary artery disease, as one disease entity under the CVDs umbrella, had a prevalence of 7.2% among adults in the United States and incurred a financial burden of 360 billion US dollars in the years 2016–2017. The introduction of artificial intelligence (AI) and machine learning over the last two decades has unlocked new dimensions in the field of cardiovascular medicine. From automatic interpretations of heart rhythm disorders via smartwatches, to assisting in complex decision-making, AI has quickly expanded its realms in medicine and has demonstrated itself as a promising tool in helping clinicians guide treatment decisions. Understanding complex genetic interactions and developing clinical risk prediction models, advanced cardiac imaging, and improving mortality outcomes are just a few areas where AI has been applied in the domain of coronary artery disease. Through this review, we sought to summarize the advances in AI relating to coronary artery disease, current limitations, and future perspectives.

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Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography

Cited by 59 publications

References 51 publications

Elevated Coronary Artery Calcium Quantified by a Validated Deep Learning Model From Lung Cancer Radiotherapy Planning Scans Predicts Mortality

Elevated Coronary Artery Calcium Quantified by a Validated Deep Learning Model From Lung Cancer Radiotherapy Planning Scans Predicts Mortality

A new and automated risk prediction of coronary artery disease using clinical endpoints and medical imaging-derived patient-specific insights: protocol for the retrospective GeoCAD cohort study

Current and Future Applications of Artificial Intelligence in Coronary Artery Disease

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