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Cited by 101Radiomics and Machine Learning in Medical ImagingForensic Anthropology and Bioarchaeology StudiesMachine Learning in Healthcare

Deep Learning to Estimate Biological Age From Chest Radiographs

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Raghu, Vineet K., Weiss, Jakob, Hoffmann, Udo, Aerts, Hugo J.W.L., Lu, Michael T.·2021·JACC: Cardiovascular Imaging
DOI 10.1016/j.jcmg.2021.01.008PMC PMC11381093
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Abstract

Objectives The goal of this study was to assess whether a deep learning estimate of age from a chest radiograph image (CXR-Age) can predict longevity beyond chronological age. Background Chronological age is an imperfect measure of longevity. Biological age, a measure of overall health, may improve personalized care. This paper proposes a new way to estimate biological age using a convolutional neural network that takes as input a CXR image and outputs a chest x-ray age (in years) as a measure of long-term mortality risk. Methods CXR-Age was developed using CXR from 116,035 individuals and validated in 2 held-out testing sets: 1) 75% of the CXR arm of PLCO (Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial) (N = 40,967); and 2) the CXR arm of NLST (National Lung Screening Trial) (N = 5,414). CXR-Age was compared to chronological age and a multivariable regression model of chronological age, risk factors, and radiograph findings to predict all-cause and cardiovascular mortality with a maximum 23 years and 13 years of follow-up, respectively. The primary outcome was observed mortality; results are provided for the testing datasets only. Results In the PLCO testing dataset, a 5-year increase in CXR-Age carried a higher risk of all-cause mortality than a 5-year increase in chronological age (CXR-Age hazard ratio [HR]: 2.26 [95% confidence interval (CI): 2.24 to 2.29] vs. chronological age HR: 1.77 [95% CI: 1.75 to 1.78]; p < 0.001). A similar pattern was found for cardiovascular mortality (CXR-Age cause-specific HR: 2.45 per 5 years [95% CI: 2.34 to 2.56] vs. chronological age HR: 1.82 per 5 years [95% CI: 1.74 to 1.90]). Similar results were seen for both outcomes in the NLST external testing dataset. Adding CXR-Age to the multivariable model resulted in significant improvements for predicting both outcomes in both testing datasets (p < 0.001 for all comparisons). Conclusions Based on a CXR image, CXR-Age predicted long-term all-cause and cardiovascular mortality.

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  • Deep learning can capture imaging features beyond traditional findings (e.g. enlarged heart silhouette, lung nodules) with valuable prognostic and potentially actionable information.

    “the results of this and previously published studies(10–12) suggest that deep learning can capture imaging features beyond traditional findings (e.g. enlarged heart silhouette, lung nodules) with valuable prognostic and potentially actionable information.”

    From Deep Learning to Estimate Cardiovascular Risk From Chest Radiographs (2024) · cites this paper as [12]

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  1. [23]United States Social Security Administration. Actuarial Life Table. Vol. 2019; 2016.

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Authors
Raghu, V. K., Weiss, J., Hoffmann, U., Aerts, H. J. W. L., & Lu, M. T.
Affiliations
Brigham and Women's Hospital (US)
Corresponding author
  • Raghu, Vineet K.
  • Weiss, Jakob
ORCID
Venue
JACC: Cardiovascular Imaging
Publisher
Elsevier BV
Published
2021-11
Volume
14
Issue
11
Pages
2226-2236
ISSN
  • 1876-7591 (ISSN-L)
  • 1936-878X
DOI
10.1016/j.jcmg.2021.01.008
PMC
PMC11381093
OpenAlex
W3137360389
Cited by
101
References
30
Citations by year
2026: 22 · 2025: 26 · 2024: 17 · 2023: 21 · 2022: 11 · 2021: 4
FWCI
6.86
Type
journal article
Language
en
Open access
Licence
Locations
  • JACC. Cardiovascular imaging (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • Research Publications (Maastricht University) (submittedVersion, other-oa)
Funders
  • Kowa Company (T32HL076136)
  • National Institutes of Health (5K24HL113128)
  • American Heart Association (18UNPG34030172, 810966)
  • Abbott Laboratories
  • Duke University
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Social Sciences › Arts and Humanities › Archeology › Forensic Anthropology and Bioarchaeology Studies
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords
biological age estimation, chest radiograph, mortality prediction, all-cause mortality, cardiovascular mortality, chronological age, convolutional neural networks, longevity, lung cancer screening, prostate cancer screening, risk stratification, individualized care, cardiovascular risk factors, external validation, model generalization, model interpretability, model explainability, model fairness, model robustness
MeSH terms
Deep Learning, Adult, Aging, Child, Preschool, Humans, Lung Neoplasms, Male, Predictive Value of Tests, Early Detection of Cancer, Young Adult

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