## 4 Discussion
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Study** & **Imaging Modality** & **Adrenal Lesions** & **ML Model** & **Accuracy (\%)** & **Specificity (\%)** & **Recall (\%)** \\ \hline \multirow{3}{*}{This Study} & T2W, T1W-CS, T1-CE MRI & 206 Adenomas, 45 Non- Adenomas & FCOS + Voting & \multirow{3}{*}{87.45 Classifier} & \multirow{3}{*}{87.33} & \multirow{3}{*}{87.63} \\  & T1W-CS MRI & 29 Adenomas, 15 Metastases & & & & \\ \cline{1-1} \cline{5-6} \multirow{3}{*}{[PERSON] et al. [26]} & T2W, T1-CS MRI & 20 Pheochromocytoma & & & & \\ \cline{1-1} \cline{5-6} \multirow{3}{*}{[PERSON] et al. [28]} & T2W, T1-CS MRI & 20 Pheochromocytoma & & & & \\ \cline{1-1} \cline{5-6} \multirow{3}{*}{[PERSON] et al. [44]} & multiphased CT & 24 Non-adenomas & & & & \\ \cline{1-1} \cline{5-6}  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of similar studies on adrenal lesion classification, detailing the dataset composition (imaging modality, and number and type of lesions), machine learning approach, and key evaluation metrics (accuracy, specificity, and recall). This table highlights methodological differences and performance variations across studies, providing a contextual benchmark for this study’s proposed pipeline. a substantial number of false negatives. Specifically, 103 non-adenomas slices were misclassified as adenomas and 53 were incorrectly classified as no lesion, suggesting that the model is biased towards adenoma detection. While this aligns with our primary focus, it remains a limitation that must be addressed in future work. This poor performance in non-adenoma classification can be attributed to two main factors. One contributing factor is dataset imbalance--non-adenomas constitute only 17.2% of the total lesions--limiting the model's exposure to this class during training. Another challenge is the significant feature overlap between adenomas and certain non-adenoma lesions (e.g., pheochromocytomas and metastases), as both can exhibit similar imaging characteristics, thereby complicating differentiation. To address these issues, future work should focus on increasing the representation of non-adenoma lesions through dataset expansion or data augmentation techniques. Additionally, incorporating external validation datasets, exploring alternative classifiers, and integrating supplementary imaging features or clinical data may enhance the model's ability to distinguish between adenomas and non-adenomas. Second, our pipeline does not include normal patients (i.e., patients without any adrenal lesions). This omission limits our ability to assess the false positive rate (FPR) for non-lesion slices in a real clinical setting, where normal scans are common. Based on our confusion matrix (Figure 2a), only 8.76% of non-lesion slices were incorrectly classified as adenomas or non-adenomas. However, these non-lesion slices were extracted from patients with adrenal lesions, meaning they do not necessarily represent true normal adrenal anatomy, as the slices may not even include the adrenal glands. As a result, the FPR for non-lesion slices in a real clinical setting could be higher than our reported value. Future studies should include normal patients to provide a more comprehensive evaluation of the pipeline's performance in clinical practice. Third, although our method eliminates the need for manual lesion segmentation, enhancing clinical applicability, it also means that lesion localization is less precise than segmentation-based approaches. This trade-off should be considered when comparing our pipeline to radiomics-based methods that rely on manually segmented regions of interest. The pipeline's performance could be further improved by incorporating a lesion segmentation step, which would provide more precise lesion localization and potentially enhance classification accuracy. However, this was not possible due to the lack of lesion segmentation annotations in our dataset.