Table 5.
Comparison of our developed model with other automated adrenal lesion classification systems using CT images
| Study | Method | Number of classes | Purpose | Subjects | Performance matrices (%) |
|---|---|---|---|---|---|
| Elmohr et al. [10] | Intensity- and geometry-based texture feature extraction and random forest classifier | 2 (tumor and control) | Differentiating large adrenal cortical tumors | 54 |
Accuracy: 82.00 Sensitivity: 81.00 Specificity: 83.00 |
| Stanzione et al. [11] | Radiomic feature extraction, recursive feature selection, and extra tree classification | 2 (solid lesion and control) | Classifying indeterminate solid adrenal lesions | 55 |
Precision: 92.00 Recall: 91.00 F1-score: 91.00 |
| Yi et al. [38] | Radiomic feature extraction, Lasso feature selection | 2 (lipid-poor adenoma and subclinical pheochromocytoma) | Differentiating pheochromocytoma and lipid-poor adenoma in adrenal incidentalomas | 265 | AUC: 90.70 |
| Moawad et al. [12] | Texture feature extraction, recursive feature elimination, and random forest classification | 2 (lesion and control) | Differentiating indeterminate small adrenal tumors | 181 |
AUC: 85.00 Sensitivity: 84.20 Specificity: 71.40 |
| Yi et al. [14] | Texture feature extraction, feature selection, and logistic multiple-regression classification | 2 (subclinical pheochromocytoma and lipid-poor adenoma) | Differentiating pheochromocytoma and lipid-poor adenoma in adrenal incidentalomas | 108 |
Accuracy: 94.40 Sensitivity: 86.20 Specificity: 97.50 |
| Robinson-Weiss et al. [39] | Deep learning-based segmentation and classification | 2 (normal and adrenal masses) | Adrenal gland segmentation and normal/adrenal mass classification | 251 and 991 | Development dataset |
|
Dice: 0.80 (normal) 0.84 (masses) Sensitivity: 83 Specificity: 89 | |||||
| Test dataset | |||||
|
Dice: 0.89 (normal) 0.89 (masses) Sensitivity: 69 Specificity: 91 | |||||
| Our method | Image segmentation, texture feature extraction using exemplar CS-LBP, NCA-based feature selection, hyperparameter-tuned kNN classification | 4 (normal, pheochromocytoma, lipid-poor adenoma, and metastasis) | Classifying adrenal gland lesions | 96 |
Accuracy: 99.87 Sensitivity: 99.92 Specificity: 99.96 Precision: 99.83 F-measure: 99.88 Geometric mean: 99.94 |