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Identifying vulnerable plaques: A 3D carotid plaque radiomics model based on HRMRI

BACKGROUND: Identification of vulnerable carotid plaque is important for the treatment and prevention of stroke. In previous studies, plaque vulnerability was assessed qualitatively. We aimed to develop a 3D carotid plaque radiomics model based on high-resolution magnetic resonance imaging (HRMRI) t...

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Autores principales: Zhang, Xun, Hua, Zhaohui, Chen, Rui, Jiao, Zhouyang, Shan, Jintao, Li, Chong, Li, Zhen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9908750/
https://www.ncbi.nlm.nih.gov/pubmed/36779063
http://dx.doi.org/10.3389/fneur.2023.1050899
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author Zhang, Xun
Hua, Zhaohui
Chen, Rui
Jiao, Zhouyang
Shan, Jintao
Li, Chong
Li, Zhen
author_facet Zhang, Xun
Hua, Zhaohui
Chen, Rui
Jiao, Zhouyang
Shan, Jintao
Li, Chong
Li, Zhen
author_sort Zhang, Xun
collection PubMed
description BACKGROUND: Identification of vulnerable carotid plaque is important for the treatment and prevention of stroke. In previous studies, plaque vulnerability was assessed qualitatively. We aimed to develop a 3D carotid plaque radiomics model based on high-resolution magnetic resonance imaging (HRMRI) to quantitatively identify vulnerable plaques. METHODS: Ninety patients with carotid atherosclerosis who underwent HRMRI were randomized into training and test cohorts. Using the radiological characteristics of carotid plaques, a traditional model was constructed. A 3D carotid plaque radiomics model was constructed using the radiomics features of 3D T(1)-SPACE and its contrast-enhanced sequences. A combined model was constructed using radiological and radiomics characteristics. Nomogram was generated based on the combined models, and ROC curves were utilized to assess the performance of each model. RESULTS: 48 patients (53.33%) were symptomatic and 42 (46.67%) were asymptomatic. The traditional model was constructed using intraplaque hemorrhage, plaque enhancement, wall remodeling pattern, and lumen stenosis, and it provided an area under the curve (AUC) of 0.816 vs. 0.778 in the training and testing sets. In the two cohorts, the 3D carotid plaque radiomics model and the combined model had an AUC of 0.915 vs. 0.835 and 0.957 vs. 0.864, respectively. In the training set, both the radiomics model and the combination model outperformed the traditional model, but there was no significant difference between the radiomics model and the combined model. CONCLUSIONS: HRMRI-based 3D carotid radiomics models can improve the precision of detecting vulnerable carotid plaques, consequently improving risk classification and clinical decision-making in patients with carotid stenosis.
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spelling pubmed-99087502023-02-10 Identifying vulnerable plaques: A 3D carotid plaque radiomics model based on HRMRI Zhang, Xun Hua, Zhaohui Chen, Rui Jiao, Zhouyang Shan, Jintao Li, Chong Li, Zhen Front Neurol Neurology BACKGROUND: Identification of vulnerable carotid plaque is important for the treatment and prevention of stroke. In previous studies, plaque vulnerability was assessed qualitatively. We aimed to develop a 3D carotid plaque radiomics model based on high-resolution magnetic resonance imaging (HRMRI) to quantitatively identify vulnerable plaques. METHODS: Ninety patients with carotid atherosclerosis who underwent HRMRI were randomized into training and test cohorts. Using the radiological characteristics of carotid plaques, a traditional model was constructed. A 3D carotid plaque radiomics model was constructed using the radiomics features of 3D T(1)-SPACE and its contrast-enhanced sequences. A combined model was constructed using radiological and radiomics characteristics. Nomogram was generated based on the combined models, and ROC curves were utilized to assess the performance of each model. RESULTS: 48 patients (53.33%) were symptomatic and 42 (46.67%) were asymptomatic. The traditional model was constructed using intraplaque hemorrhage, plaque enhancement, wall remodeling pattern, and lumen stenosis, and it provided an area under the curve (AUC) of 0.816 vs. 0.778 in the training and testing sets. In the two cohorts, the 3D carotid plaque radiomics model and the combined model had an AUC of 0.915 vs. 0.835 and 0.957 vs. 0.864, respectively. In the training set, both the radiomics model and the combination model outperformed the traditional model, but there was no significant difference between the radiomics model and the combined model. CONCLUSIONS: HRMRI-based 3D carotid radiomics models can improve the precision of detecting vulnerable carotid plaques, consequently improving risk classification and clinical decision-making in patients with carotid stenosis. Frontiers Media S.A. 2023-01-26 /pmc/articles/PMC9908750/ /pubmed/36779063 http://dx.doi.org/10.3389/fneur.2023.1050899 Text en Copyright © 2023 Zhang, Hua, Chen, Jiao, Shan, Li and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neurology
Zhang, Xun
Hua, Zhaohui
Chen, Rui
Jiao, Zhouyang
Shan, Jintao
Li, Chong
Li, Zhen
Identifying vulnerable plaques: A 3D carotid plaque radiomics model based on HRMRI
title Identifying vulnerable plaques: A 3D carotid plaque radiomics model based on HRMRI
title_full Identifying vulnerable plaques: A 3D carotid plaque radiomics model based on HRMRI
title_fullStr Identifying vulnerable plaques: A 3D carotid plaque radiomics model based on HRMRI
title_full_unstemmed Identifying vulnerable plaques: A 3D carotid plaque radiomics model based on HRMRI
title_short Identifying vulnerable plaques: A 3D carotid plaque radiomics model based on HRMRI
title_sort identifying vulnerable plaques: a 3d carotid plaque radiomics model based on hrmri
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9908750/
https://www.ncbi.nlm.nih.gov/pubmed/36779063
http://dx.doi.org/10.3389/fneur.2023.1050899
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