Cardiac magnetic resonance radiomics: basic principles and clinical perspectives
Cardiac magnetic resonance radiomics: basic principles and clinical perspectives
Radiomics is a novel image analysis technique, whereby voxel-level information is extracted from digital images and used to derive multiple numerical quantifiers of shape and tissue character. Cardiac magnetic resonance (CMR) is the reference imaging modality for assessment of cardiac structure and function. Conventional analysis of CMR scans is mostly reliant on qualitative image analysis and basic geometric quantifiers. Small proof-of-concept studies have demonstrated the feasibility and superior diagnostic accuracy of CMR radiomics analysis over conventional reporting. CMR radiomics has the potential to transform our approach to defining image phenotypes and, through this, improve diagnostic accuracy, treatment selection, and prognostication. The purpose of this article is to provide an overview of radiomics concepts for clinicians, with particular consideration of application to CMR. We will also review existing literature on CMR radiomics, discuss challenges, and consider directions for future work.
cardiac magnetic resonance, image-based diagnosis, machine learning, radiomics, texture analysis
349-356
Raisi-Estabragh, Zahra
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Izquierdo, Cristian
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Campello, Victor M.
70b294e4-d5f3-4f65-9d26-d0d6c6c8227d
Martin-Isla, Carlos
7501fb82-b913-4b2a-b0e0-19ccc9a4e60c
Jaggi, Akshay
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Harvey, Nicholas
ce487fb4-d360-4aac-9d17-9466d6cba145
Lekadir, Karim
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Petersen, Steffen E.
04f2ce88-790d-48dc-baac-cbe0946dd928
1 April 2020
Raisi-Estabragh, Zahra
43c85c5e-4574-476b-80d6-8fb1cdb3df0a
Izquierdo, Cristian
6aea4a1f-2fd5-4700-acf5-0c9c07a326a0
Campello, Victor M.
70b294e4-d5f3-4f65-9d26-d0d6c6c8227d
Martin-Isla, Carlos
7501fb82-b913-4b2a-b0e0-19ccc9a4e60c
Jaggi, Akshay
3c44b68c-526b-43d4-932e-8dac54a91fa8
Harvey, Nicholas
ce487fb4-d360-4aac-9d17-9466d6cba145
Lekadir, Karim
b8de558a-869c-4574-b0d3-005dc52c3106
Petersen, Steffen E.
04f2ce88-790d-48dc-baac-cbe0946dd928
Raisi-Estabragh, Zahra, Izquierdo, Cristian, Campello, Victor M., Martin-Isla, Carlos, Jaggi, Akshay, Harvey, Nicholas, Lekadir, Karim and Petersen, Steffen E.
(2020)
Cardiac magnetic resonance radiomics: basic principles and clinical perspectives.
European Heart Journal, 21 (4), .
(doi:10.1093/ehjci/jeaa028).
Abstract
Radiomics is a novel image analysis technique, whereby voxel-level information is extracted from digital images and used to derive multiple numerical quantifiers of shape and tissue character. Cardiac magnetic resonance (CMR) is the reference imaging modality for assessment of cardiac structure and function. Conventional analysis of CMR scans is mostly reliant on qualitative image analysis and basic geometric quantifiers. Small proof-of-concept studies have demonstrated the feasibility and superior diagnostic accuracy of CMR radiomics analysis over conventional reporting. CMR radiomics has the potential to transform our approach to defining image phenotypes and, through this, improve diagnostic accuracy, treatment selection, and prognostication. The purpose of this article is to provide an overview of radiomics concepts for clinicians, with particular consideration of application to CMR. We will also review existing literature on CMR radiomics, discuss challenges, and consider directions for future work.
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More information
Accepted/In Press date: 6 February 2020
e-pub ahead of print date: 6 March 2020
Published date: 1 April 2020
Additional Information:
Funding Information:
This article is supported by the London Medical Imaging and Artificial Intelligence Centre for Value Based Healthcare (AI4VBH), which is funded from the Data to Early Diagnosis and Precision Medicine strand of the government’s Industrial Strategy Challenge Fund, managed and delivered by Innovate UK on behalf of UK Research and Innovation (UKRI). Views expressed are those of the authors and not necessarily those of the AI4VBH Consortium members, the NHS, Innovate UK, or UKRI.
Funding Information:
Z.R.E. was supported by a British Heart Foundation Clinical Research Training Fellowship (FS/17/81/33318). A.J. was supported by a Fulbright Predoctoral Research Award (2019-2020). S.E.P. acknowledges support from the National Institute for Health Research (NIHR) Cardiovascular Biomedical Research Centre at Barts NHS Trust and has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825903 (euCanSHare project). S.E.P. acknowledges support from the ‘SmartHeart’ EPSRC programme grant (www.nihr.ac.uk; EP/P001009/1). S.E.P. also acknowledges support from the CAP-AI programme, London’s first AI enabling programme focused on stimulating growth in the capital’s AI Sector. CAP-AI is led by Capital Enterprise in partnership with Barts Health NHS Trust and Digital Catapult and is funded by the European Regional Development Fund and Barts Charity. SEP also acts as a paid consultant to Circle Cardiovascular Imaging Inc., Calgary, Canada and Servier.
Publisher Copyright:
© 2020 The Author(s) 2020. Published by Oxford University Press on behalf of the European Society of Cardiology.
Keywords:
cardiac magnetic resonance, image-based diagnosis, machine learning, radiomics, texture analysis
Identifiers
Local EPrints ID: 437790
URI: http://eprints.soton.ac.uk/id/eprint/437790
ISSN: 0195-668X
PURE UUID: 2b3ed1d8-4586-4ab0-9719-5cf58a628e11
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Date deposited: 17 Feb 2020 17:31
Last modified: 17 Mar 2024 05:18
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Contributors
Author:
Zahra Raisi-Estabragh
Author:
Cristian Izquierdo
Author:
Victor M. Campello
Author:
Carlos Martin-Isla
Author:
Akshay Jaggi
Author:
Karim Lekadir
Author:
Steffen E. Petersen
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