### Related Work
#### 1.4.1 Radiomics-Based ML Pipelines: Adenomas vs. Other Adrenal Lesions  
Most papers in this area have developed radiomics-based ML pipelines to differentiate between adrenal adenomas and other adrenal lesions. [PERSON] et al.'s was the only study to compare MRI and CT datasets when applying a logistic regression model using shape-based, first- and second-order radiomic features to classify lipid-poor adenomas and carcinomas [25]. Other studies utilized a logistic regression model to differentiate metastases from adenomas using only first-order [26] and metastases from lipid-poor adenomas using shaped-based and first-order radiomic features [27], in this case lipid-poor adenomas. [PERSON] et al. also used first-order radiomic features to fit a dataset of adenomas and pheochromocytomas with a support vector machine (SVM) model [28]. [PERSON] et al. attempted a multiclass scenario to differentiate between lipid-poor, lipid-rich, and non-adenoma lesions. They extracted first-and second-order features and classified them with a decision tree [29]. Despite their varied approaches, these studies share limitations, including small (fewer than 70 lesions) and imbalanced MRI datasets, specifically of T1 Weighted Chemical Shift (CS)--In Phase (IP) and Out of Phase (OOP)--and T2 Weighted (T2W) images.  
Other studies followed a similar approach using CT images, primarily analyzing multiphase CT to differentiate lipid-poor adrenal adenomas from pheochromocytomas [7; 30; 31] and adenomas from carcinomas [32; 33]. [PERSON] et al. was the only study that applied an unsupervised ML model--KMeans--to classify first- and second-order radiomic features, though their dataset contained only 19 lesions [32]. [PERSON] et al. extracted radiomic features across all categories and tested two different ML models, achieving the performance with Boruta Random Forest [33]. [PERSON] et al. also experimented with multiple models, and their multi-layer perceptron achieving the best results using first-order, shape-based, and second-order features [30]. [PERSON] et al. innovated by incorporating clinical data (eg., necrosis changes) alongside first-order radiomic features. The authors also tested multiple ML models, and the best was the logistic regression [7]. [PERSON] et al. also applied logistic regression to classify first-, second-, and higher-order radiomic features [31]. UnenhancedCT images were used to distinguish between adenomas and metastases [34], lipid-poor adenomas and pheochromocytomas [35], and adenomas and other unspecified lesions [36]. In addition, [PERSON] et al. trained a logistic regression with second-order features for multi-class classification with a dataset with lipid-rich and lipid-poor adenomas, metastases, and pheochromocytomas [37]. [PERSON] et al. also trained a logistic regression but only with first-order features [34]. [PERSON] et al. followed a similar approach in their studies [31; 35]. [PERSON] et al. focused their models on analyzing higher-order radiomic features and used an LASSO regression model for feature selection [36].  
Studies leveraging MRI and CT data have extensively explored radiomics-based ML pipelines to differentiate adenomas from other lesions. MRI-focused studies have primarily targeted lipid-poor and lipid-rich adenomas and pheochromocytomas, employing logistic regression, SVM, and decision trees to classify first- and second-order features. Similarly, CT-based studies have demonstrated the effectiveness of radiomics for differentiating adenomas from carcinomas, metastases, and pheochromocytomas, with logistic regression, random forest, and MLP models frequently achieving high accuracy. Despite their potential, these studies share common limitations, including small and imbalanced datasets, reliance on manual segmentation, and variability in feature extraction techniques.