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Texture analysis of T2-weighted cardiovascular magnetic resonance imaging to discriminate between cardiac amyloidosis and hypertrophic cardiomyopathy

BACKGROUND: To elucidate the value of texture analysis (TA) in detecting and differentiating myocardial tissue alterations on T2-weighted CMR (cardiovascular magnetic resonance imaging) in patients with cardiac amyloidosis (CA) and hypertrophic cardiomyopathy (HCM). METHODS: In this retrospective st...

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Autores principales: Huang, Shan, Shi, Ke, Zhang, Yi, Yan, Wei-Feng, Guo, Ying-Kun, Li, Yuan, Yang, Zhi-Gang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9124433/
https://www.ncbi.nlm.nih.gov/pubmed/35597906
http://dx.doi.org/10.1186/s12872-022-02671-0
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author Huang, Shan
Shi, Ke
Zhang, Yi
Yan, Wei-Feng
Guo, Ying-Kun
Li, Yuan
Yang, Zhi-Gang
author_facet Huang, Shan
Shi, Ke
Zhang, Yi
Yan, Wei-Feng
Guo, Ying-Kun
Li, Yuan
Yang, Zhi-Gang
author_sort Huang, Shan
collection PubMed
description BACKGROUND: To elucidate the value of texture analysis (TA) in detecting and differentiating myocardial tissue alterations on T2-weighted CMR (cardiovascular magnetic resonance imaging) in patients with cardiac amyloidosis (CA) and hypertrophic cardiomyopathy (HCM). METHODS: In this retrospective study, 100 CA (58.5 ± 10.7 years; 41 (41%) females) and 217 HCM (50.7 ± 14.8 years, 101 (46.5%) females) patients who underwent CMR scans were included. Regions of interest for TA were delineated by two radiologists independently on T2-weighted imaging (T2WI). Stepwise dimension reduction and texture feature selection based on reproducibility, machine learning algorithms, and correlation analyses were performed to select features. Both the CA and HCM groups were randomly divided into a training dataset and a testing dataset (7:3). After the TA model was established in the training set, the diagnostic performance of the model was validated in the testing set and further validated in a subgroup of patients with similar hypertrophy. RESULTS: The 7 independent texture features provided, in combination, a diagnostic accuracy of 86.0% (AUC = 0.915; 95% CI 0.879–0.951) in the training dataset and 79.2% (AUC = 0.842; 95% CI 0.759–0.924) in the testing dataset. The differential diagnostic accuracy in the similar hypertrophy subgroup was 82.2% (AUC = 0.864, 95% CI 0.805–0.922). The significance of the difference between the AUCs of the TA model and late gadolinium enhancement (LGE) was verified by Delong’s test (p = 0.898). All seven texture features showed significant differences between CA and HCM (all p < 0.001). CONCLUSIONS: Our study demonstrated that texture analysis based on T2-weighted images could feasibly differentiate CA from HCM, even in patients with similar hypertrophy. The selected final texture features could achieve a comparable diagnostic capacity to the quantification of LGE. Trial registration Since this study is a retrospective observational study and no intervention had been involved, trial registration is waived. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12872-022-02671-0.
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spelling pubmed-91244332022-05-23 Texture analysis of T2-weighted cardiovascular magnetic resonance imaging to discriminate between cardiac amyloidosis and hypertrophic cardiomyopathy Huang, Shan Shi, Ke Zhang, Yi Yan, Wei-Feng Guo, Ying-Kun Li, Yuan Yang, Zhi-Gang BMC Cardiovasc Disord Research BACKGROUND: To elucidate the value of texture analysis (TA) in detecting and differentiating myocardial tissue alterations on T2-weighted CMR (cardiovascular magnetic resonance imaging) in patients with cardiac amyloidosis (CA) and hypertrophic cardiomyopathy (HCM). METHODS: In this retrospective study, 100 CA (58.5 ± 10.7 years; 41 (41%) females) and 217 HCM (50.7 ± 14.8 years, 101 (46.5%) females) patients who underwent CMR scans were included. Regions of interest for TA were delineated by two radiologists independently on T2-weighted imaging (T2WI). Stepwise dimension reduction and texture feature selection based on reproducibility, machine learning algorithms, and correlation analyses were performed to select features. Both the CA and HCM groups were randomly divided into a training dataset and a testing dataset (7:3). After the TA model was established in the training set, the diagnostic performance of the model was validated in the testing set and further validated in a subgroup of patients with similar hypertrophy. RESULTS: The 7 independent texture features provided, in combination, a diagnostic accuracy of 86.0% (AUC = 0.915; 95% CI 0.879–0.951) in the training dataset and 79.2% (AUC = 0.842; 95% CI 0.759–0.924) in the testing dataset. The differential diagnostic accuracy in the similar hypertrophy subgroup was 82.2% (AUC = 0.864, 95% CI 0.805–0.922). The significance of the difference between the AUCs of the TA model and late gadolinium enhancement (LGE) was verified by Delong’s test (p = 0.898). All seven texture features showed significant differences between CA and HCM (all p < 0.001). CONCLUSIONS: Our study demonstrated that texture analysis based on T2-weighted images could feasibly differentiate CA from HCM, even in patients with similar hypertrophy. The selected final texture features could achieve a comparable diagnostic capacity to the quantification of LGE. Trial registration Since this study is a retrospective observational study and no intervention had been involved, trial registration is waived. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12872-022-02671-0. BioMed Central 2022-05-21 /pmc/articles/PMC9124433/ /pubmed/35597906 http://dx.doi.org/10.1186/s12872-022-02671-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Huang, Shan
Shi, Ke
Zhang, Yi
Yan, Wei-Feng
Guo, Ying-Kun
Li, Yuan
Yang, Zhi-Gang
Texture analysis of T2-weighted cardiovascular magnetic resonance imaging to discriminate between cardiac amyloidosis and hypertrophic cardiomyopathy
title Texture analysis of T2-weighted cardiovascular magnetic resonance imaging to discriminate between cardiac amyloidosis and hypertrophic cardiomyopathy
title_full Texture analysis of T2-weighted cardiovascular magnetic resonance imaging to discriminate between cardiac amyloidosis and hypertrophic cardiomyopathy
title_fullStr Texture analysis of T2-weighted cardiovascular magnetic resonance imaging to discriminate between cardiac amyloidosis and hypertrophic cardiomyopathy
title_full_unstemmed Texture analysis of T2-weighted cardiovascular magnetic resonance imaging to discriminate between cardiac amyloidosis and hypertrophic cardiomyopathy
title_short Texture analysis of T2-weighted cardiovascular magnetic resonance imaging to discriminate between cardiac amyloidosis and hypertrophic cardiomyopathy
title_sort texture analysis of t2-weighted cardiovascular magnetic resonance imaging to discriminate between cardiac amyloidosis and hypertrophic cardiomyopathy
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9124433/
https://www.ncbi.nlm.nih.gov/pubmed/35597906
http://dx.doi.org/10.1186/s12872-022-02671-0
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