ArticleEuropean radiology2023
Contrast-enhanced CT radiomics improves the prediction of abdominal aortic aneurysm progression.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 19 citations in OpenAlex.
- Application of radiomics in abdominal aortic aneurysm and endovascular aneurysm repair-related adverse events imaging: a systematic review.CVIR endovascular · 2026Review
- Deep learning algorithm for predicting rapid progression of abdominal aortic aneurysm by integrating CT images and clinical features.Scientific reports · 2025Article
- Artificial intelligence-based machine learning protocols enable quicker assessment of aortic biomechanics: A case study.Journal of vascular surgery cases and innovative techniques · 2025Article
- Abdominal aortic aneurysm classification based on dynamic intraluminal thrombus analysis during cardiac cycle.Journal of vascular surgery cases and innovative techniques · 2025Article
- Prognosis prediction in type B aortic intramural hematoma patients using a combined model based on aortic computed tomography angiography radiomics.Quantitative imaging in medicine and surgery · 2025Article
- Predicting the growth of asymptomatic small abdominal aortic aneurysms (AAA) based on deep learning.Frontiers in physiology · 2025Article
- Computational Hemodynamics-Based Growth Prediction for Small Abdominal Aortic Aneurysms: Laminar Simulations Versus Large Eddy Simulations.Annals of biomedical engineering · 2024Article
- Automatic segmentation of intraluminal thrombosis of abdominal aortic aneurysms from CT angiography using a mixed-scale-driven multiview perception network (MComputers in biology and medicine · 2024Article
- Radiomic-based Textural Analysis of Intraluminal Thrombus in Aortic Abdominal Aneurysms: A Demonstration of Automated Workflow.Journal of cardiovascular translational research · 2023Article
- Characterization of small abdominal aortic aneurysms' growth status using spatial pattern analysis of aneurismal hemodynamics.Scientific reports · 2023Article
- Medical Radiology: Current Progress.Diagnostics (Basel, Switzerland) · 2023Review
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10 authors at 4 institutions in 2 countries.
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Abstract
objectivesTo determine if three-dimensional (3D) radiomic features of contrast-enhanced CT (CECT) images improve prediction of rapid abdominal aortic aneurysm (AAA) growth.
methodsThis longitudinal cohort study retrospectively analyzed 195 consecutive patients (mean age, 72.4 years ± 9.1) with a baseline CECT and a subsequent CT or MR at least 6 months later. 3D radiomic features were measured for 3 regions of the AAA, viz. the vessel lumen only; the intraluminal thrombus (ILT) and aortic wall only; and the entire AAA sac (lumen, ILT, and wall). Multiple machine learning (ML) models to predict rapid growth, defined as the upper tercile of observed growth (> 0.25 cm/year), were developed using data from 60% of the patients. Diagnostic accuracy was evaluated using the area under the receiver operating characteristic curve (AUC) in the remaining 40% of patients.
resultsThe median AAA maximum diameter was 3.9 cm (interquartile range [IQR], 3.3-4.4 cm) at baseline and 4.4 cm (IQR, 3.7-5.4 cm) at the mean follow-up time of 3.2 ± 2.4 years (range, 0.5-9 years). A logistic regression model using 7 radiomic features of the ILT and wall had the highest AUC (0.83; 95% confidence interval [CI], 0.73-0.88) in the development cohort. In the independent test cohort, this model had a statistically significantly higher AUC than a model including maximum diameter, AAA volume, and relevant clinical factors (AUC = 0.78, 95% CI, 0.67-0.87 vs AUC = 0.69, 95% CI, 0.57-0.79; p = 0.04).
conclusionA radiomics-based method focused on the ILT and wall improved prediction of rapid AAA growth from CECT imaging. KEY POINTS: • Radiomic analysis of 195 abdominal CECT revealed that an ML-based model that included textural features of intraluminal thrombus (if present) and aortic wall improved prediction of rapid AAA progression compared to maximum diameter. • Predictive accuracy was higher when radiomic features were obtained from the thrombus and wall as opposed to the entire AAA sac (including lumen), or the lumen alone. • Logistic regression of selected radiomic features yielded similar accuracy to predict rapid AAA progression as random forests or support vector machines.
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