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Computerized Differentiation of Growth Status for Abdominal Aortic Aneurysms: A Feasibility Study

Journal of Cardiovascular Translational Research (2023)Cite this article

Abstract 

Fast-growing abdominal aortic aneurysms (AAA) have a high rupture risk and poor outcomes if not promptly identified and treated. Our primary objective is to improve the differentiation of small AAAs’ growth status (fast versus slow-growing) through a combination of patient health information, computational hemodynamics, geometric analysis, and artificial intelligence. 3D computed tomography angiography (CTA) data available for 70 patients diagnosed with AAAs with known growth status were used to conduct geometric and hemodynamic analyses. Differences among ten metrics (out of ninety metrics) were statistically significant discriminators between fast and slow-growing groups. Using a support vector machine (SVM) classifier, the area under receiving operating curve (AUROC) and total accuracy of our best predictive model for differentiation of AAAs’ growth status were 0.86 and 77.50%, respectively. In summary, the proposed analytics has the potential to differentiate fast from slow-growing AAAs, helping guide resource allocation for the management of patients with AAAs.

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Data Availability

Imaging data were acquired at Mayo Clinic (Rochester, MN, USA). Derived data supporting this study will be made availble from the corresponding author (JJ) upon request.

Abbreviations

AAA:

Abdominal aortic aneurysm

AUROC:

Area under the receiver operatingor characteristic curves

CFD:

Computational fluid dynamics

DVO:

Degree of volume overlap

ILT:

Intraluminal thrombosis

MI:

Myocardial infarction

MIS:

Minimal inscribed sphere

NRVx :

Normalized remaining volume at x% of (MIS) sphere cutoff

NTT1:

Normalized thrombosis thickness 1

NTT2:

Normalized thrombosis thickness 2

NTTD:

Normalized thrombosis thickness differences

NTNU1:

Normalized thrombosis nonuniformity 1

NTNU2:

Normalized thrombosis nonuniformity 2

OSI:

Oscillatory shear index

OSI-Std:

Oscillatory shear index standard deviation

PHI:

Patient health information

SA-OSI:

Spatially averaged oscillatory shear index

STA-WSS:

Spatially and temporally averaged wall shear stress

TA-DVO:

Temporally averaged degree of volume overlap

TA-NOV:

Temporally averaged number of vortices

TA-WSSMax:

Temporally averaged wall shear stress maximum

TA-WSSMin:

Temporally averaged wall shear stress minimum

TIA:

Transient ischemic attack

UI:

Undulation index

VDC:

Voronoi diagram core

VtV:

Vortex volume to AAA volume

WSS:

Wall shear stress

WSS-Std:

Wall shear stress standard deviation

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Funding

Mr. Mostafa Rezaeitaleshmahalleh is partially supported by a fellowship from the Health Research Institute at Michigan Technological University. This study also benefits from technologies developed by an NIH grant (R01-EB029570A1). 

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Michigan Technological University, MI, Houghton, USA

    Mostafa Rezaeitaleshmahalleh, Kevin W. Sunderland, Zonghan Lyu, Tonie Johnson, Kristin King & Jingfeng Jiang

  2. Joint Center for Biocomputing and Digital Health, Health Research Institute and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, MI, USA

    Mostafa Rezaeitaleshmahalleh, Kevin W. Sunderland, Zonghan Lyu, Tonie Johnson, Kristin King & Jingfeng Jiang

  3. Department of Cardiovascular Medicine, Mayo Clinic, MN, Rochester, USA

    David A. Liedl, Janet M. Hofer, Wiktoria Kuczmik & Robert D. McBane II

  4. Department of Management Science and Statistics, The University of Texas at San Antonio, San Antonio, TX, USA

    Min Wang

  5. Department of Radiology, Mayo Clinic, MN, Rochester, USA

    Xiaoming Zhang & Jingfeng Jiang

  6. Division of Vascular and Endovascular Surgery, Mayo Clinic, Rochester, MN, USA

    Todd E. Rasmussen

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  1. Mostafa Rezaeitaleshmahalleh
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Corresponding author

Correspondence to Jingfeng Jiang.

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Ethics Approval and Consent to Participate

This study was approved by Institutional Review Boards at Michigan Technological University and Mayo Clinic. Because this is a secondary analysis of existing imaging data, patient consent was not required.

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The authors declare no competing interests.

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Associate Editor Judith C. Sluimer oversaw the review of this article

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Rezaeitaleshmahalleh, M., Sunderland, K.W., Lyu, Z. et al. Computerized Differentiation of Growth Status for Abdominal Aortic Aneurysms: A Feasibility Study. J. of Cardiovasc. Trans. Res. (2023). https://doi.org/10.1007/s12265-022-10352-8

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  • DOI: https://doi.org/10.1007/s12265-022-10352-8

Keywords

  • Machine learning
  • Predictive modeling
  • Abdominal aortic aneurysm
  • Aneurysm Growth
  • Computational hemodynamics

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