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

The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.

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

Open access
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
Open this paper in CoChat — free accountOpen PDF

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41598-017-01931-w

Cites

64 of 64 references verified0 citation errors · 0 not checkable

Statistics

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

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 64 references

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.

Cited by

Sorted by citations

Items 101–176 of 176

Related

See all

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review