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Cited by 176Radiomics and Machine Learning in Medical ImagingMachine Learning in HealthcareArtificial Intelligence in Healthcare and Education

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Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework

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Oakden-Rayner, Luke, Carneiro, Gustavo, Bessen, Taryn, Nascimento, Jacinto C., Bradley, Andrew P., Palmer, Lyle J.·2017·Scientific Reports
DOI 10.1038/s41598-017-01931-wPMC PMC5431941
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Abstract

Precision medicine approaches rely on obtaining precise knowledge of the true state of health of an individual patient, which results from a combination of their genetic risks and environmental exposures. This approach is currently limited by the lack of effective and efficient non-invasive medical tests to define the full range of phenotypic variation associated with individual health. Such knowledge is critical for improved early intervention, for better treatment decisions, and for ameliorating the steadily worsening epidemic of chronic disease. We present proof-of-concept experiments to demonstrate how routinely acquired cross-sectional CT imaging may be used to predict patient longevity as a proxy for overall individual health and disease status using computer image analysis techniques. Despite the limitations of a modest dataset and the use of off-the-shelf machine learning methods, our results are comparable to previous 'manual' clinical methods for longevity prediction. This work demonstrates that radiomics techniques can be used to extract biomarkers relevant to one of the most widely used outcomes in epidemiological and clinical research - mortality, and that deep learning with convolutional neural networks can be usefully applied to radiomics research. Computer image analysis applied to routinely collected medical images offers substantial potential to enhance precision medicine initiatives.

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Crossref · 10.1038/s41598-017-01931-w

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Figures

Figure 1 Manhattan plot showing the distribution of covariates in each tissue segment. The dotted line identifies the threshold of significance (the −log10 of the p-value is plotted on the y-axis, covariates above this line have p-values < 0.05).
Figure 1 Manhattan plot showing the distribution of covariates in each tissue segment. The dotted line identifies the threshold of significance (the −log10 of the p-value is plotted on the y-axis, covariates above this line have p-values < 0.05).CC BY · PubMed Central
Figure 2 Kaplan-Meier curves demonstrating 5-year survival for the high and low risk phenotypes developed using human engineered image features extracted from CT chest scans. There were two mortalities for the twenty five patients identified to be low risk, and twenty mortalities amongst the twenty three patients identified as high risk. The difference between curves is significant (p < 0.000005).
Figure 2 Kaplan-Meier curves demonstrating 5-year survival for the high and low risk phenotypes developed using human engineered image features extracted from CT chest scans. There were two mortalities for the twenty five patients identified to be low risk, and twenty mortalities amongst the twenty three patients identified as high risk. The difference between curves is significant (p < 0.000005).CC BY · PubMed Central
Figure 3 Pooled ROC curves of the 6-fold cross validation experiment to predict 5 year mortality and survival outcomes from CT chest imaging, comparing the deep learning model and the human feature engineering/random forest model.
Figure 3 Pooled ROC curves of the 6-fold cross validation experiment to predict 5 year mortality and survival outcomes from CT chest imaging, comparing the deep learning model and the human feature engineering/random forest model.CC BY · PubMed Central
Figure 4 Images at the level of the proximal left anterior descending coronary artery, with the most strongly predicted mortality and survival cases selected by averaging the predictions from the deep learning and engineered feature models. The mortality cases (left side) demonstrate prominent visual changes of emphysema, cardiomegaly, vascular disease and osteopaenia. The survival cases (right side) appear visually less diseased and frail.
Figure 4 Images at the level of the proximal left anterior descending coronary artery, with the most strongly predicted mortality and survival cases selected by averaging the predictions from the deep learning and engineered feature models. The mortality cases (left side) demonstrate prominent visual changes of emphysema, cardiomegaly, vascular disease and osteopaenia. The survival cases (right side) appear visually less diseased and frail.CC BY · PubMed Central
Figure 5 Schematic of the deep learning architecture for the mortality classification task.
Figure 5 Schematic of the deep learning architecture for the mortality classification task.CC BY · PubMed Central
Figure 6 Diagrammatic representation of the analysis pipelines with engineered features and deep learning methods.
Figure 6 Diagrammatic representation of the analysis pipelines with engineered features and deep learning methods.CC BY · PubMed Central

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Research works printed but not linked: 3

  1. Wilkens, L. R. & Lee, J. Nutritional epidemiology. (Wiley Online Library, 1998).
  2. Zeiler, M. D. & Fergus, R. In European Conference on Computer Vision. 818–833 (Springer).
  3. Nair, V. & Hinton, G. E. In Proceedings of the 27th International Conference on Machine Learning (ICML-10). 807–814.

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Metadata

Authors
Oakden-Rayner, L., Carneiro, G., Bessen, T., Nascimento, J. C., Bradley, A. P., & Palmer, L. J.
Affiliations
  • The University of Adelaide (AU)
  • Royal Adelaide Hospital (AU)
  • Instituto Superior Técnico (PT)
  • The University of Queensland (AU)
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2017-05-10
Volume
7
Issue
1
Pages
1648-1648
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-017-01931-w
PMC
PMC5431941
OpenAlex
W2611902073
Cited by
154
References
64
Citations by year
2026: 6 · 2025: 7 · 2024: 11 · 2023: 19 · 2022: 19 · 2021: 24 · 2020: 20 · 2019: 13 · 2018: 25 · 2017: 10
FWCI
14.27
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • QUT ePrints (Queensland University of Technology) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
radiomics, longevity prediction, mortality prediction, feature engineering, convolutional neural networks, precision medicine, biomarkers, computer image analysis, chronic disease
MeSH terms
Deep Learning, Diagnostic Imaging, Humans, Image Processing, Computer-Assisted, Longevity, Mortality, Phenotype, Radiology, Risk Factors, ROC Curve, Task Performance and Analysis, Tomography, X-Ray Computed, Reproducibility of Results, Area Under Curve, Kaplan-Meier Estimate, Precision Medicine

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