Repeatability of cardiac magnetic resonance radiomics: a multi-centre multi-vendor test-retest study
Repeatability of cardiac magnetic resonance radiomics: a multi-centre multi-vendor test-retest study
Aims: to evaluate the repeatability of cardiac magnetic resonance (CMR) radiomics features on test-retest scanning using a multi-centre multi-vendor dataset with a varied case-mix.
Methods and results: the sample included 54 test-retest studies from the VOLUMES resource (thevolumesresource.com). Images were segmented according to a pre-defined protocol to select three regions of interest (ROI) in end-diastole and end-systole: right ventricle, left ventricle (LV), and LV myocardium. We extracted radiomics shape features from all three ROIs and, additionally, first-order and texture features from the LV myocardium. Overall, 280 features were derived per study. For each feature, we calculated intra-class correlation coefficient (ICC), within-subject coefficient of variation, and mean relative difference. We ranked robustness of features according to mean ICC stratified by feature category, ROI, and cardiac phase, demonstrating a wide range of repeatability. There were features with good and excellent repeatability (ICC ≥ 0.75) within all feature categories and ROIs. A high proportion of first-order and texture features had excellent repeatability (ICC ≥ 0.90), however, these categories also contained features with the poorest repeatability (ICC < 0.50).
Conclusion: CMR radiomic features have a wide range of repeatability. This paper is intended as a reference for future researchers to guide selection of the most robust features for clinical CMR radiomics models. Further work in larger and richer datasets is needed to further define the technical performance and clinical utility of CMR radiomics.
Raisi-Estabragh, Zahra
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Gkontra, Polyxeni
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Jaggi, Akshay
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Cooper, Jackie
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Augusto, João
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Bhuva, Anish N.
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Davies, Rhodri H.
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Manisty, Charlotte H.
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Moon, James C.
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Munroe, Patricia B.
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Harvey, Nicholas C.
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Lekadir, Karim
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Petersen, Steffen E.
04f2ce88-790d-48dc-baac-cbe0946dd928
2 December 2020
Raisi-Estabragh, Zahra
43c85c5e-4574-476b-80d6-8fb1cdb3df0a
Gkontra, Polyxeni
bf8e2eda-7fb2-4de0-b884-edd345e2712d
Jaggi, Akshay
3c44b68c-526b-43d4-932e-8dac54a91fa8
Cooper, Jackie
9a4e035c-3fb5-486f-a681-11b4e29adbd6
Augusto, João
63e65e49-ac49-4b32-9b49-8a03ef345459
Bhuva, Anish N.
6fabb0f5-b9cd-403d-8bdd-09a759e5696f
Davies, Rhodri H.
62977976-1144-4afe-bd42-a1b4f97891c2
Manisty, Charlotte H.
7965f8f5-350d-4d57-9bdb-8a0bb5f9aa8d
Moon, James C.
1177b3af-3296-478c-83fc-31a560a8fda7
Munroe, Patricia B.
44d23746-20cd-4572-860e-7350424cc031
Harvey, Nicholas C.
ce487fb4-d360-4aac-9d17-9466d6cba145
Lekadir, Karim
b8de558a-869c-4574-b0d3-005dc52c3106
Petersen, Steffen E.
04f2ce88-790d-48dc-baac-cbe0946dd928
Raisi-Estabragh, Zahra, Gkontra, Polyxeni, Jaggi, Akshay, Cooper, Jackie, Augusto, João, Bhuva, Anish N., Davies, Rhodri H., Manisty, Charlotte H., Moon, James C., Munroe, Patricia B., Harvey, Nicholas C., Lekadir, Karim and Petersen, Steffen E.
(2020)
Repeatability of cardiac magnetic resonance radiomics: a multi-centre multi-vendor test-retest study.
Frontiers in Cardiovascular Medicine, 7, [586236].
(doi:10.3389/fcvm.2020.586236).
Abstract
Aims: to evaluate the repeatability of cardiac magnetic resonance (CMR) radiomics features on test-retest scanning using a multi-centre multi-vendor dataset with a varied case-mix.
Methods and results: the sample included 54 test-retest studies from the VOLUMES resource (thevolumesresource.com). Images were segmented according to a pre-defined protocol to select three regions of interest (ROI) in end-diastole and end-systole: right ventricle, left ventricle (LV), and LV myocardium. We extracted radiomics shape features from all three ROIs and, additionally, first-order and texture features from the LV myocardium. Overall, 280 features were derived per study. For each feature, we calculated intra-class correlation coefficient (ICC), within-subject coefficient of variation, and mean relative difference. We ranked robustness of features according to mean ICC stratified by feature category, ROI, and cardiac phase, demonstrating a wide range of repeatability. There were features with good and excellent repeatability (ICC ≥ 0.75) within all feature categories and ROIs. A high proportion of first-order and texture features had excellent repeatability (ICC ≥ 0.90), however, these categories also contained features with the poorest repeatability (ICC < 0.50).
Conclusion: CMR radiomic features have a wide range of repeatability. This paper is intended as a reference for future researchers to guide selection of the most robust features for clinical CMR radiomics models. Further work in larger and richer datasets is needed to further define the technical performance and clinical utility of CMR radiomics.
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fcvm-07-586236
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Accepted/In Press date: 2 November 2020
Published date: 2 December 2020
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Local EPrints ID: 488880
URI: http://eprints.soton.ac.uk/id/eprint/488880
ISSN: 2297-055X
PURE UUID: 24a0e88c-59e1-4de7-a8d2-0d42e6e2e1e2
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Date deposited: 09 Apr 2024 10:00
Last modified: 10 Apr 2024 01:40
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Contributors
Author:
Zahra Raisi-Estabragh
Author:
Polyxeni Gkontra
Author:
Akshay Jaggi
Author:
Jackie Cooper
Author:
João Augusto
Author:
Anish N. Bhuva
Author:
Rhodri H. Davies
Author:
Charlotte H. Manisty
Author:
James C. Moon
Author:
Patricia B. Munroe
Author:
Karim Lekadir
Author:
Steffen E. Petersen
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