## 4 Discussion  
In this study, we developed a two-stage ML pipeline for automated adrenal lesion detection and binary classification (non-adenoma vs. adenoma) in MRI images across multiple sequences. Each stage was trained separately using the same inter-patient split and the same training, validation, and testing patients to avoid data leakage within and between stages. After independently validating each stage, we evaluated the complete pipeline on the full test set in a real-world scenario, where all detected lesions were passed to the classifier without additional filtering. To provide a contextual benchmark for our study, we selected similar works from the literature to compare methodologies and results. From the studies that use radiomics-based feature extraction and classification with traditional ML, we selected two that focus on differentiating adenomas from other adrenal lesions (1) and analyzed MRI datasets (2). Studies that exclusively analyzed lipid-poor adenomas were excluded, as our dataset did not contain a significant number of such cases. For deep learning-based studies, since  
Figure 4: Examples of incorrect detection and/or classifications for different patients. All presented images are T1-Weighted Chemical Shift (Out-of-Phase). GT: Ground truth; Pred: Predicted. Green bounding boxes are the ground truth; red is the high-score prediction. Subfigures are labeled (**a**–**f**) for reference. (**a**) GT: Non-lesion; Pred: Non-adenoma; (**b**) GT: Non-lesion; Pred: Adenoma; (**c**) GT: Non-adenoma; Pred: No lesion; (**d**) GT: Non-adenoma; Pred: Adenoma; (**e**) GT: Adenoma; Pred: Non-lesion; (**f**) GT: Adenoma; Pred: Non-adenoma. no prior work specifically analyzed MRI images, we selected one study based on its similar objective to ours but that analyzes CT images. Table 10 summarizes the imaging modality, dataset characteristics, machine learning models, and key evaluation metrics (accuracy, specificity, and recall) of these studies, highlighting methodological differences and performance variations. Compared to the similar studies in Table 10, our proposed pipeline demonstrates competitive performance while addressing key limitations of prior research. Our dataset contains 206 adenomas and 45 non-adenomas, making it significantly larger than those used in [PERSON] et al. [26] (29 adenomas, 15 metastases) and [PERSON] et al. [28] (38 adenomas, 20 pheochromocytoma), both of which suffered from small and highly imbalanced datasets. These studies employed logistic regression and SVM, respectively, on manually extracted radiomic features, whereas our pipeline integrates deep learning-based lesion detection (FCOS) with an ensemble classifier, enabling fully automated processing without manual segmentation. [PERSON] et al. [44] achieved higher accuracy (94%), but their dataset was limited to 83 adenomas and 24 non-adenomas, and they used multiphase CT. Our pipeline achieves a high specificity (87.33%), indicating strong performance in correctly identifying non-adenomas (i.e., non-adenoma lesions + non-lesion images), which was a key challenge in previous ML-based studies. While our proposed pipeline demonstrates strong performance in automated adrenal lesion classification, several limitations should be stated. First, our dataset, although larger than previous MRI-based studies, remains relatively small for deep learning standards, which affects detection performance in the first stage of our pipeline. Additionally, it is imbalanced in terms of lesion types, which impacts classification performance in the second stage. A closer examination of the overall end-to-end confusion matrix (see Figure 2a) reveals that the recall (true positive rate) for non-adenomas was only 29.41%, indicating