Precision radiology: predicting longevity using feature engineering and deep learning methods in a radiomics framework
Open accessAbstract
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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Deep learning is applied in radiomics.
“Deep learning is applied in radiotherapy [89], in PET-MRI attenuation correction [90, 91], in radiomics [92, 93]”
From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [93]
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Prime Time for Artificial Intelligence in Interventional Radiology
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Review of Applications of Artificial Intelligence Algorithms in Pavement Management
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Artificial Intelligence at the Service of Precision Medicine
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Understanding the Research Landscape of Deep Learning in Biomedical Science: Scientometric Analysis
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Machine‐learning algorithm that can improve the diagnostic accuracy of septic arthritis of the knee
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Artificial Intelligence and Health Care
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Artificial intelligence in radiation oncology
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- 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)
- Venue
- Scientific Reports
- Published
- 2017-05-10
- Volume
- 7
- Issue
- 1
- Pages
- 1648-1648
- ISSN
- 2045-2322 (ISSN-L)
- 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
- gold
- doi.org
- 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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