## 4 Discussion
Cumulative predictions over such extended periods could enable stratified monitoring strategies, focusing resources on patients at higher risk of rapid growth and optimising clinical resource allocation. Previous studies have explored automated segmentation methods to model AAA growth using morphometric characteristics, biomarkers and computational fluid dynamics (CFD) in conjunction with ML methods [7; 8; 9]. [PERSON] et al. described the use of a ML model to predict AAA growth using aneurysm area and major axis diameter derived from serial computed tomography angiography (CTA) studies [50]. [PERSON] et al. developed a ML model able to predict AAA diameter to within 2 mm in 85% and 71% of patients at 12 and 24 months, respectively. They used ultrasound to explore flow-mediated dilatation (FMD) of the brachial artery as a biomarker in combination with AAA diameter [51]. [PERSON] et al. investigated a ML algorithm encompassing auto-segmentation of AAA on CTA and prediction of rupture risk (9). This used a generalised additive model (GAM) with six geometric and one biomechanical marker yielding an accuracy of 87%, 78% sensitivity and 92% specificity for AAA rupture risk classification [52]. More recently, [PERSON] et al. demonstrated that a linear regression model derived from geometric features, automatically calculated from deep learning created segmentations, which could predict AAA growth to within 2 mm in 87% of cases on CT [53]. However, due to the novel nature of the current model, no meaningful direct comparison was able to be made with prior work. Work by [PERSON] et al. [54] investigated the use of radiomic features from perivascular adipose tissue (PVAT) to enhance predictive performance for AAA growth. Their study demonstrated that growing AAAs post-endovascular aneurysm repair (EVAR) exhibited significantly higher surface area-to-volume ratios (0.70 vs. 0.63, \(p\) = 0.04) and more heterogeneous PVAT texture, with differences in dependence variance (29.88 vs. 26.96, \(p\) = 0.01) and long-run emphasis (4.76 vs. 4.20, \(p\) = 0.01). Reported findings mirror previous research in coronary artery disease, where PVAT radiomic features have been shown to improve risk assessment for atherosclerotic progression. In CAD, heterogeneous PVAT texture has been associated with increased inflammatory activity and vascular remodelling, leading to the development of novel risk stratification methods [55,56]. By analogy, for incidentally detected AAAs, as examined in this cohort, PVAT may reflect similar inflammatory or mechanical changes in the vascular environment that are not captured by aneurysm sac analysis alone. Integrating texture features from surrounding tissues like PVAT could there provide a more comprehensive understanding of aneurysm behaviour and progression. Furthermore, texture analysis, particularly metrics derived from grey-level co-occurrence matrix (GLCM) and grey-level dependence matrix (GLDM), has also been increasingly recognised for its role in predicting aneurysm behaviour [54,57]. For instance, studies have shown that texture heterogeneity in thrombi and PVAT can reflect inflammatory activity and vascular remodelling, which may contribute to aneurysm progression. Integrating texture features from surrounding tissues like PVAT could therefore provide a more comprehensive understanding of aneurysm beyond traditional morphometric measures. The study by [PERSON] et al. further highlighted the advantages of combining radiomic features with clinical data in a multi-modal predictive model, which outperformed models using radiomic or clinical data alone. Their clinico-radiological model achieved an area under the curve (AUC) of 0.78 (95% CI: 0.65-0.91), significantly higher than models using radiomic or clinical features alone (AUC = 0.69 for each) [58]. These findings may suggest a merit to incorporating clinical parameters, such as cholesterol levels, blood pressure, and patient history, to enhance the predictive accuracy of future models.