Key result
An integrated clinico-radiological model incorporating perivascular adipose tissue radiomic features predicted abdominal aortic aneurysm growth after endovascular repair with an AUC of 0.78.
Why the study?
The relationship between radiomic features of perivascular adipose tissue and abdominal aortic aneurysm growth after endovascular aneurysm repair was unknown.
Does an integrated clinico-radiological model using radiomic features of perivascular adipose tissue predict abdominal aortic aneurysm growth after endovascular repair?
Observational (n=79)
Radiologists blinded to clinical information
No
Does an integrated clinico-radiological model using radiomic features of perivascular adipose tissue predict abdominal aortic aneurysm growth after endovascular repair?
Effect estimate: AUC 0.78 (95% CI 0.65-0.91)
Absolute Event Rate: 0.78% vs 0.69%
p-value: p=<0.01
Radiomic features of perivascular adipose tissue, when integrated with clinical features, can help predict abdominal aortic aneurysm progression after endovascular repair.
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.
No takes yet. Share an insight, caveat, or question.
Lv et al. (2024) conducted an observational in Abdominal aortic aneurysm after endovascular aneurysm repair (n=79). Integrated clinico-radiological model (including PVAT radiomic features) vs. Clinical features alone was evaluated on Prediction of AAA growth (Area Under the Curve) (AUC 0.78, 95% CI 0.65-0.91, p=<0.01). An integrated clinico-radiological model incorporating perivascular adipose tissue radiomic features predicted abdominal aortic aneurysm growth after endovascular repair with an AUC of 0.78.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: