## 4 Discussion  
This study tested several machine learning models using radiomic features derived from [18F]FDG PET-CT to predict the growth rate of abdominal aortic aneurysms. Overall, predictive accuracy was highest for a regression-MLP model. To the best of our knowledge,  
Figure 5: Violin plots demonstrating uncertainty on the model trained with full feature set (**a**) and reduced feature set (**b**). Model uncertainty was generally higher when trained using the reduced feature set relative to the full feature set. Note: MAE: mean absolute error. there are no previous studies to have investigated the use of [18F]FDG PET-CT to predict the growth rate of abdominal aortic aneurysms. Conventional PET-CT metrics, such as SUVmax, MTV, and TLG, are well-established in oncological imaging for quantifying metabolic activity. SUVmax is frequently used to assess tumour aggressiveness and response to therapy in a variety of cancers [40; 41; 42]. Similarly, MTV and TLG have been shown to predict treatment outcomes in an oncological setting, reflecting tumour burden and metabolic activity, respectively [43]. However, their application in vascular disease, including AAA prediction, remains limited and under-explored. Moreover, studies investigating SUV metrics for predicting AAA growth and rupture have reported conflicting results, likely due to variability in imaging protocols and methodologies, which complicates their clinical utility in this context [12; 13; 14; 15; 16; 17]. By contrast, there is growing evidence supporting the predictive capability of textural radiomic parameters in various cardiovascular disorders, offering nuanced insights into tissue heterogeneity that conventional metrics may overlook [44; 45]. Radiomic features may therefore offer an advanced alternative by providing a detailed analysis of texture and structural patterns, capturing subtle imaging biomarkers that reflect underlying pathophysiology. This enables a deeper understanding of aneurysm and improves predictive accuracy compared to traditional metrics such as SUV. The regression-MLP model was trained to minimise the mean squared error loss function, evaluated between predicted and ground truth aneurysm growth rates whilst avoiding overfitting of the training data. Each fully connected layer of the MLP is preceded by a dropout layer, which acts as a regulariser and prevents the model from overfitting to the training data. The models trained with the full set of [18F]FDG PET-CT features extracted and the reduced set identified using RF-RFE, both resulted in very similar average growth rate prediction accuracy of 1.35 mm/year on the internal dataset. Dimensionality reduction with RF-RFE showed no tangible improvement in the growth rate prediction accuracy, although the uncertainty quantification experiments revealed that using a reduced set of features increased model uncertainty (relative to using the full feature set). Based on these results, it can be inferred that models trained using the full set of features are more likely to predict AAA growth rates to a higher degree of confidence, than those trained using the reduced feature set. When evaluated on an unseen external dataset, the model achieved an accuracy of 1.8 mm/year, a result that falls within the 2 mm margin of accuracy typically considered acceptable for technical variability [46; 47]. Consistency across internal and external datasets suggests the model's potential for real-world application. Given that more rapid AAA growth is associated with a higher rupture rate this model could provide a useful risk stratification metric [8; 9; 48]. The RESCAN study, a meta-analysis of surveillance intervals for small AAAs, reported an average annual growth rate of 1-2 mm per year [49]. While the reported error margin in our study is comparable to this observed yearly growth rate, potentially limiting its utility for precise short-term predictions, the model remains promising for longer-term predictions, such as over 5 or 10 years.