Table 1.
Breakdown of article characteristics
| Characteristics | n (%) |
|---|---|
| Publication year | |
| 2021 | 106 |
| 2022 | 151 |
| Journal | |
| European Radiology | 195 (75.9%) |
| European Journal of Radiology | 44 (17.1%) |
| Radiology | 12 (4.7%) |
| Radiology: Imaging Cancer | 4 (1.6%) |
| Radiology: Artificial Intelligence | 2 (0.8%) |
| Topic | |
| Abdominal | 52 (20.2%) |
| Chest | 52 (20.2%) |
| Neuroradiology | 43 (16.7%) |
| Genitourinary | 36 (14.0%) |
| Head and neck | 26 (10.1%) |
| Breast | 21 (8.2%) |
| Musculoskeletal | 15 (5.8%) |
| Cardiovascular | 4 (1.6%) |
| Oncology | 3 (1.2%) |
| Pediatric | 3 (1.2%) |
| Chest/neuroradiology | 1 (0.4%) |
| Chest/abdominal | 1 (0.4%) |
| Modality | |
| MRI | 110 (42.8%) |
| CT | 109 (42.4%) |
| PET/CT | 17 (6.6%) |
| US | 15 (5.8%) |
| Mammography | 3 (1.2%) |
| Angiography | 1 (0.4%) |
| X-ray | 1 (0.4%) |
| MRI, CT | 1 (0.4%) |
| Study design | |
| Retrospective | 248 (96.5%) |
| Prospective | 8 (3.11%) |
| Model utility | |
| Classification | 144 (56.03%) |
| Prognostication | 79 (30.74%) |
| Detection | 30 (11.67%) |
| Classification, prognostication | 4 (1.55%) |
| Model type | |
| LASSO/LR | 139 (54.4%) |
| ML classifiers* | 64 (24.9%) |
| RF** | 20 (7.7%) |
| SVM*** | 20 (7.7%) |
| DL | 11 (4.2%) |
| Not mentioned | 3 (1.1%) |
Abbreviations: LASSO least absolute shrinkage and selection operator, LR logistic regression, RF random forest, SVM support vector machine, DL deep learning, ML machine learning
*In most cases, multiple ML modeling methods were combined. Some of the methods are listed here: linear classifier, K-nearest neighbor, passive-aggressive classifier, perceptron, ridge classifier, AdaBoost, Naïve-Bayes, ElasticNet, Gradient Boosting Decision Tree, AutoML Ensemble
**The number of papers only used the random forest method for modelling, but not as a part of combined ML classifiers
***The number of papers only used the support vector machine method for modelling, but not as a part of combined ML classifiers