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

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...

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Autores principales:	Luke Oakden-Rayner, Gustavo Carneiro, Taryn Bessen, Jacinto C. Nascimento, Andrew P. Bradley, Lyle J. Palmer
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2f9f82700e384fee93b5e2cfdf3b8c55
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spelling	oai:doaj.org-article:2f9f82700e384fee93b5e2cfdf3b8c552021-12-02T15:05:58ZPrecision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework10.1038/s41598-017-01931-w2045-2322https://doaj.org/article/2f9f82700e384fee93b5e2cfdf3b8c552017-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-01931-whttps://doaj.org/toc/2045-2322Abstract 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.Luke Oakden-RaynerGustavo CarneiroTaryn BessenJacinto C. NascimentoAndrew P. BradleyLyle J. PalmerNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-13 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Luke Oakden-Rayner Gustavo Carneiro Taryn Bessen Jacinto C. Nascimento Andrew P. Bradley Lyle J. Palmer Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework
description	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.
format	article
author	Luke Oakden-Rayner Gustavo Carneiro Taryn Bessen Jacinto C. Nascimento Andrew P. Bradley Lyle J. Palmer
author_facet	Luke Oakden-Rayner Gustavo Carneiro Taryn Bessen Jacinto C. Nascimento Andrew P. Bradley Lyle J. Palmer
author_sort	Luke Oakden-Rayner
title	Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework
title_short	Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework
title_full	Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework
title_fullStr	Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework
title_full_unstemmed	Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework
title_sort	precision radiology: predicting longevity using feature engineering and deep learning methods in a radiomics framework
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/2f9f82700e384fee93b5e2cfdf3b8c55
work_keys_str_mv	AT lukeoakdenrayner precisionradiologypredictinglongevityusingfeatureengineeringanddeeplearningmethodsinaradiomicsframework AT gustavocarneiro precisionradiologypredictinglongevityusingfeatureengineeringanddeeplearningmethodsinaradiomicsframework AT tarynbessen precisionradiologypredictinglongevityusingfeatureengineeringanddeeplearningmethodsinaradiomicsframework AT jacintocnascimento precisionradiologypredictinglongevityusingfeatureengineeringanddeeplearningmethodsinaradiomicsframework AT andrewpbradley precisionradiologypredictinglongevityusingfeatureengineeringanddeeplearningmethodsinaradiomicsframework AT lylejpalmer precisionradiologypredictinglongevityusingfeatureengineeringanddeeplearningmethodsinaradiomicsframework
_version_	1718388634358382592

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

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