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Radiomic-based Textural Analysis of Intraluminal Thrombus in Aortic Abdominal Aneurysms: A Demonstration of Automated Workflow

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Journal of Cardiovascular Translational Research Aims and scope Submit manuscript

Abstract

Our main objective is to investigate how the structural information of intraluminal thrombus (ILT) can be used to predict abdominal aortic aneurysms (AAA) growth status through an automated workflow. Fifty-four human subjects with ILT in their AAAs were identified from our database; those AAAs were categorized as slowly- (< 5 mm/year) or fast-growing (≥ 5 mm/year) AAAs. In-house deep-learning image segmentation models were used to generate 3D geometrical AAA models, followed by automated analysis. All features were fed into a support vector machine classifier to predict AAA’s growth status.

The most accurate prediction model was achieved through four geometrical parameters measuring the extent of ILT, two parameters quantifying the constitution of ILT, antihypertensive medication, and the presence of co-existing coronary artery disease. The predictive model achieved an AUROC of 0.89 and a total accuracy of 83%. When ILT was not considered, our prediction’s AUROC decreased to 0.75 (P-value < 0.001).

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Data Availability

Derived Data supporting the findings of this study are available from the corresponding author (JJ) on request.

Notes

  1. https://www.rdocumentation.org/packages/caret/versions/6.0-92/topics/varImp

  2. https://cran.r-project.org/web/packages/iml/vignettes/intro.html

Abbreviations

AAA:

Abdominal Aortic Aneurysm

AUROC:

Area Under the Receiver Operator Curve

CFD:

Computational Fluid Dynamics

CTA:

Computed Tomography Angiography

CACU-Net:

Context-Aware Cascaded U-Net

GLM:

Generalized Linear Model

GLMnet:

Generalized Linear Model Lasso

ILT:

Intraluminal Thrombosis

KNN:

K-nearest neighbor

PHI:

Patient Health Information

LoG:

Laplacian of Gaussian

GLCM:

Gray Level Co-occurrence Matrix

GLRLM:

Gray Level Run Length Matrix

GLSZM:

Gray Level Size Zone Matrix

NTT2:

Normalized Thrombosis Thickness 2

ML:

Machine Learning

NTTD:

Normalized Thrombosis Thickness Differences

RF:

Random Forest (RF)

SVM:

Support Vector Machine

WSS:

Wall Shear Stress

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Acknowledgements

Mr. Mostafa Rezaeitaleshmahalleh is partially supported by research fellowship awards from the Health Research Institute of Michigan Technological University and Blue Cross Blue Shield of Michigan Foundation. A post-doctoral fellowship from the American Heart Association (23POST1022454) supports Dr. Nan Mu. The study benefitted from technologies developed under a research grant from NIH/NIBIB (R01-EB029570-A1).

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Authors and Affiliations

  1. Department of Biomedical Engineering, Michigan Technological University, 1400 Townsend Drive, Houghton, MI, USA

    Mostafa Rezaeitaleshmahalleh, Nan Mu, Zonghan Lyu & 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, Nan Mu, Zonghan Lyu & Jingfeng Jiang

  3. Department of Applied Computing, Michigan Technological University, Houghton, MI, USA

    Weihua Zhou

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

    Xiaoming Zhang & Jingfeng Jiang

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

    Todd E. Rasmussen

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

    Robert D. McBane II

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  1. Mostafa Rezaeitaleshmahalleh
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  2. Nan Mu
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Correspondence to Jingfeng Jiang.

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Rezaeitaleshmahalleh, M., Mu, N., Lyu, Z. et al. Radiomic-based Textural Analysis of Intraluminal Thrombus in Aortic Abdominal Aneurysms: A Demonstration of Automated Workflow. J. of Cardiovasc. Trans. Res. 16, 1123–1134 (2023). https://doi.org/10.1007/s12265-023-10404-7

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