A morphological indicator for aortic dissection: fitting circle of the thoracic aorta.


Journal

BMC cardiovascular disorders
ISSN: 1471-2261
Titre abrégé: BMC Cardiovasc Disord
Pays: England
ID NLM: 100968539

Informations de publication

Date de publication:
28 Aug 2024
Historique:
received: 21 03 2023
accepted: 19 08 2024
medline: 31 8 2024
pubmed: 31 8 2024
entrez: 28 8 2024
Statut: epublish

Résumé

This study aims to identify a morphological indicator of aortic dissection (AD) based on the geometrical characteristics of the thoracic aorta. We evaluated computed tomographic angiograms of 63 samples with AD (22 with type A AD, 41 with type B AD) and 71 healthy samples. Via centerline extraction and spatial transformation, the spatial entanglement of the aorta was minimized, and the expanded 2D aortic morphology was obtained. The 2D morphology of the thoracic aorta was fit to a circle. The applicability of the fitting circle method for identifying aortic dissection was verified by multivariable logistic regression analysis. Via the 3D coordinate transformation algorithm, the optimal aortic view was obtained. On this view, the geometrical characteristics of the thoracic aortas of the healthy controls were similar to a portion of a circle (sum of residuals: 3502.45 ± 2566.71, variance: 86.23 ± 56.60), while that of AD samples had poorer similarity to the circle (sum of residuals: 5404.78 ± 3891.69, variance: 129.90 ± 90.09). This difference was significant (p < 0.001). A logistic regression model showed that increased deformation of the thoracic aorta was a significant indicator of aortic dissection (odds ratio: 1.35, p = 0.034). The morphology of the healthy thoracic aorta could be fit to a circle, while that of the dissected aorta had poorer similarity to the circle. The statistics of the circle are an effective indicator of aortic deformation in AD. This study is registered in the Chinese Clinical Trial Registry (ChiCTR2000029219).

Sections du résumé

BACKGROUND BACKGROUND
This study aims to identify a morphological indicator of aortic dissection (AD) based on the geometrical characteristics of the thoracic aorta.
METHODS METHODS
We evaluated computed tomographic angiograms of 63 samples with AD (22 with type A AD, 41 with type B AD) and 71 healthy samples. Via centerline extraction and spatial transformation, the spatial entanglement of the aorta was minimized, and the expanded 2D aortic morphology was obtained. The 2D morphology of the thoracic aorta was fit to a circle. The applicability of the fitting circle method for identifying aortic dissection was verified by multivariable logistic regression analysis.
RESULTS RESULTS
Via the 3D coordinate transformation algorithm, the optimal aortic view was obtained. On this view, the geometrical characteristics of the thoracic aortas of the healthy controls were similar to a portion of a circle (sum of residuals: 3502.45 ± 2566.71, variance: 86.23 ± 56.60), while that of AD samples had poorer similarity to the circle (sum of residuals: 5404.78 ± 3891.69, variance: 129.90 ± 90.09). This difference was significant (p < 0.001). A logistic regression model showed that increased deformation of the thoracic aorta was a significant indicator of aortic dissection (odds ratio: 1.35, p = 0.034).
CONCLUSIONS CONCLUSIONS
The morphology of the healthy thoracic aorta could be fit to a circle, while that of the dissected aorta had poorer similarity to the circle. The statistics of the circle are an effective indicator of aortic deformation in AD.
TRIAL REGISTRATION BACKGROUND
This study is registered in the Chinese Clinical Trial Registry (ChiCTR2000029219).

Identifiants

pubmed: 39198782
doi: 10.1186/s12872-024-04130-4
pii: 10.1186/s12872-024-04130-4
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

461

Subventions

Organisme : Natural Science Foundation of Anhui Provincial Education Department
ID : KJ2021A0251
Organisme : Shanghai Jiao Tong University School of Medicine Doctoral Innovation Fund
ID : BXJ201935
Organisme : Translational Medicine Science and Technology Infrastructure Opening Project
ID : TMSK-2021-121
Organisme : Clinical Research Plan of SHDC
ID : SHDC2020CR6016-003
Organisme : Shanghai Ninth People's Hospital Nursing Fund Project
ID : JYHL2020MS01

Informations de copyright

© 2024. The Author(s).

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Auteurs

Hongji Pu (H)

Department of Vascular Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.

Tao Peng (T)

School of Biomedical Engineering, Anhui Medical University, Meishan Road, Shushan District, Hefei, 230032, China.

Zhijue Xu (Z)

Department of Vascular Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, 200240, China.

Qi Sun (Q)

Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.

Zixin Wang (Z)

School of Biomedical Engineering, Anhui Medical University, Meishan Road, Shushan District, Hefei, 230032, China.

Hui Ma (H)

School of Biomedical Engineering, Anhui Medical University, Meishan Road, Shushan District, Hefei, 230032, China.

Shu Fang (S)

School of Biomedical Engineering, Anhui Medical University, Meishan Road, Shushan District, Hefei, 230032, China.

Yang Yang (Y)

Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.

Jie Wu (J)

Department of Vascular Surgery, Affiliated Hospital of Guizhou Medicine University, Guizhou, 550000, China.

Ruihua Wang (R)

Department of Vascular Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.

Peng Qiu (P)

Department of Vascular Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China. hiiigh@163.com.

Jinhua Zhou (J)

School of Biomedical Engineering, Anhui Medical University, Meishan Road, Shushan District, Hefei, 230032, China. zhoujinhua@ahmu.edu.cn.

Xinwu Lu (X)

Department of Vascular Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China. luxinwu@shsmu.edu.cn.

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