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Radiomics Signatures of Carotid Plaque on Computed Tomography Angiography: An Approach to Identify Symptomatic Plaques

PURPOSE: To develop and validate a combined model incorporating conventional clinical and imaging characteristics and radiomics signatures based on head and neck computed tomography angiography (CTA) to assess plaque vulnerability. METHODS: We retrospectively analyzed 167 patients with carotid ather...

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Autores principales: Shi, Jinglong, Sun, Yu, Hou, Jie, Li, Xiaogang, Fan, Jitao, Zhang, Libo, Zhang, Rongrong, You, Hongrui, Wang, Zhenguo, Zhang, Anxiaonan, Zhang, Jianhua, Jin, Qiuyue, Zhao, Lianlian, Yang, Benqiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10654187/
https://www.ncbi.nlm.nih.gov/pubmed/37195452
http://dx.doi.org/10.1007/s00062-023-01289-9
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author Shi, Jinglong
Sun, Yu
Hou, Jie
Li, Xiaogang
Fan, Jitao
Zhang, Libo
Zhang, Rongrong
You, Hongrui
Wang, Zhenguo
Zhang, Anxiaonan
Zhang, Jianhua
Jin, Qiuyue
Zhao, Lianlian
Yang, Benqiang
author_facet Shi, Jinglong
Sun, Yu
Hou, Jie
Li, Xiaogang
Fan, Jitao
Zhang, Libo
Zhang, Rongrong
You, Hongrui
Wang, Zhenguo
Zhang, Anxiaonan
Zhang, Jianhua
Jin, Qiuyue
Zhao, Lianlian
Yang, Benqiang
author_sort Shi, Jinglong
collection PubMed
description PURPOSE: To develop and validate a combined model incorporating conventional clinical and imaging characteristics and radiomics signatures based on head and neck computed tomography angiography (CTA) to assess plaque vulnerability. METHODS: We retrospectively analyzed 167 patients with carotid atherosclerosis who underwent head and neck CTA and brain magnetic resonance imaging (MRI) within 1 month. Clinical risk factors and conventional plaque characteristics were evaluated, and radiomic features were extracted from the carotid plaques. The conventional, radiomics and combined models were developed using fivefold cross-validation. Model performance was evaluated using receiver operating characteristic (ROC), calibration, and decision curve analyses. RESULTS: Patients were divided into symptomatic (n = 70) and asymptomatic (n = 97) groups based on MRI results. Homocysteine (odds ratio, OR 1.057; 95% confidence interval, CI 1.001–1.116), plaque ulceration (OR 6.106; 95% CI 1.933–19.287), and carotid rim sign (OR 3.285; 95% CI 1.203–8.969) were independently associated with symptomatic status and were used to construct the conventional model and s radiomic features were retained to establish the radiomics model. Radiomics scores incorporated with conventional characteristics were used to establish the combined model. The area under the ROC curve (AUC) of the combined model was 0.832, which outperformed the conventional (AUC = 0.767) and radiomics (AUC = 0.797) models. Calibration and decision curves analysis showed that the combined model was clinically useful. CONCLUSION: Radiomics signatures of carotid plaque on CTA can well predict plaque vulnerability, which may provide additional value to identify high-risk patients and improve outcomes. SUPPLEMENTARY INFORMATION: The online version of this article (10.1007/s00062-023-01289-9) contains supplementary material, which is available to authorized users.
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spelling pubmed-106541872023-05-17 Radiomics Signatures of Carotid Plaque on Computed Tomography Angiography: An Approach to Identify Symptomatic Plaques Shi, Jinglong Sun, Yu Hou, Jie Li, Xiaogang Fan, Jitao Zhang, Libo Zhang, Rongrong You, Hongrui Wang, Zhenguo Zhang, Anxiaonan Zhang, Jianhua Jin, Qiuyue Zhao, Lianlian Yang, Benqiang Clin Neuroradiol Original Article PURPOSE: To develop and validate a combined model incorporating conventional clinical and imaging characteristics and radiomics signatures based on head and neck computed tomography angiography (CTA) to assess plaque vulnerability. METHODS: We retrospectively analyzed 167 patients with carotid atherosclerosis who underwent head and neck CTA and brain magnetic resonance imaging (MRI) within 1 month. Clinical risk factors and conventional plaque characteristics were evaluated, and radiomic features were extracted from the carotid plaques. The conventional, radiomics and combined models were developed using fivefold cross-validation. Model performance was evaluated using receiver operating characteristic (ROC), calibration, and decision curve analyses. RESULTS: Patients were divided into symptomatic (n = 70) and asymptomatic (n = 97) groups based on MRI results. Homocysteine (odds ratio, OR 1.057; 95% confidence interval, CI 1.001–1.116), plaque ulceration (OR 6.106; 95% CI 1.933–19.287), and carotid rim sign (OR 3.285; 95% CI 1.203–8.969) were independently associated with symptomatic status and were used to construct the conventional model and s radiomic features were retained to establish the radiomics model. Radiomics scores incorporated with conventional characteristics were used to establish the combined model. The area under the ROC curve (AUC) of the combined model was 0.832, which outperformed the conventional (AUC = 0.767) and radiomics (AUC = 0.797) models. Calibration and decision curves analysis showed that the combined model was clinically useful. CONCLUSION: Radiomics signatures of carotid plaque on CTA can well predict plaque vulnerability, which may provide additional value to identify high-risk patients and improve outcomes. SUPPLEMENTARY INFORMATION: The online version of this article (10.1007/s00062-023-01289-9) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2023-05-17 2023 /pmc/articles/PMC10654187/ /pubmed/37195452 http://dx.doi.org/10.1007/s00062-023-01289-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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/) .
spellingShingle Original Article
Shi, Jinglong
Sun, Yu
Hou, Jie
Li, Xiaogang
Fan, Jitao
Zhang, Libo
Zhang, Rongrong
You, Hongrui
Wang, Zhenguo
Zhang, Anxiaonan
Zhang, Jianhua
Jin, Qiuyue
Zhao, Lianlian
Yang, Benqiang
Radiomics Signatures of Carotid Plaque on Computed Tomography Angiography: An Approach to Identify Symptomatic Plaques
title Radiomics Signatures of Carotid Plaque on Computed Tomography Angiography: An Approach to Identify Symptomatic Plaques
title_full Radiomics Signatures of Carotid Plaque on Computed Tomography Angiography: An Approach to Identify Symptomatic Plaques
title_fullStr Radiomics Signatures of Carotid Plaque on Computed Tomography Angiography: An Approach to Identify Symptomatic Plaques
title_full_unstemmed Radiomics Signatures of Carotid Plaque on Computed Tomography Angiography: An Approach to Identify Symptomatic Plaques
title_short Radiomics Signatures of Carotid Plaque on Computed Tomography Angiography: An Approach to Identify Symptomatic Plaques
title_sort radiomics signatures of carotid plaque on computed tomography angiography: an approach to identify symptomatic plaques
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10654187/
https://www.ncbi.nlm.nih.gov/pubmed/37195452
http://dx.doi.org/10.1007/s00062-023-01289-9
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