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

Mostafa Rezaeitaleshmahalleh, Nan Mu, Zonghan Lyu, Weihua Zhou, Xiaoming Zhang, Todd E. Rasmussen, Robert D. McBane, Jingfeng Jiang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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). Graphical Abstract: [Figure not available: see fulltext.]

Original languageEnglish
Pages (from-to)1123-1134
Number of pages12
JournalJournal of Cardiovascular Translational Research
Volume16
Issue number5
DOIs
StatePublished - Oct 2023
Externally publishedYes

Keywords

  • Abdominal aortic aneurysm
  • Growth
  • Machine Learning
  • Predictive Modeling
  • Radiomics
  • Thrombosis

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