AI Article Synopsis
- A study looked at how certain features of fat around blood vessels (called PVAT) relate to the growth of a type of bulging blood vessel called an abdominal aortic aneurysm (AAA) after a specific surgery (EVAR).
- The researchers checked 79 patients and found that those whose AAAs were growing had fat characteristics that were different from those whose AAAs were not growing.
- They discovered that by combining fat features and other health info, they could better predict if an AAA would get worse, showing that the fat around the vessel may help doctors know what to expect.
Article Abstract
Objectives: The study aimed to investigate the relationship between the radiomic features of perivascular adipose tissue (PVAT) and abdominal aortic aneurysm (AAA) growth after endovascular aneurysm repair (EVAR).
Methods: Patients with sub-renal AAA who underwent regular follow-up after EVAR between March 2014 and March 2024 were retrospectively collected. Two radiologists segmented aneurysms and PVAT. Patients were categorised into growing and non-growing groups based on volumetric changes observed in two follow-up computed tomography examinations. One hundred seven radiomic features were automatically extracted from the PVAT region. Univariable and multivariable logistic regression was performed to analyse radiomic features and clinical characteristics. Furthermore, the performance of the integrated clinico-radiological model was compared with models using only radiomic features or clinical characteristics separately.
Results: A total of 79 patients (68 ± 9 years, 89% men) were enroled in this study, 19 of whom had a growing aneurysm. Compared to the non-growing group, PVAT of growing AAA showed a higher surface area to volume ratio (non-growing vs growing, 0.63 vs 0.70, p = 0.04), and a trend of low dependence and high dispersion manifested by texture features (p < 0.05). The area under the curve of the integrated clinico-radiological model was 0.78 (95% confidence intervals 0.65-0.91), with a specificity of 87%. The integrated model outperformed models using only radiomic or clinical features separately (0.78 vs 0.69 vs 0.69).
Conclusions: Higher surface area to volume ratio and more heterogeneous texture presentation of PVAT were associated with aneurysm dilation after EVAR. Radiomic features of PVAT have the potential to predict AAA progression.
Clinical Relevance Statement: Radiomic features of PVAT are associated with AAA progression and can be an independent risk factor for aneurysm dilatation to assist clinicians in postoperative patient surveillance and management.
Key Points: After EVAR for AAA, patients require monitoring for progression. PVAT surrounding growing AAA after EVAR exhibits a more heterogeneous texture. Integrating PVAT-related features and clinical features results in better predictive performance.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11442904 | PMC |
| http://dx.doi.org/10.1186/s13244-024-01804-7 | DOI Listing |
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