| AI | Artificial Intelligence |
| AUC | Area Under the Curve |
| CART | Classification and Regression Tree |
| CBR | Clinico-Biological-Radiomics |
| CT | Computed Tomography |
| DL | Deep Learning |
| DLR | Deep Learning Radiomics |
| DLNMS | Deep Learning Nodal Metastasis Signature |
| EBUS | Endobronchial Ultrasound |
| ESMO | European Society for Medical Oncology |
| ESTS | European Society of Thoracic Surgeons |
| FDG | Fluorodeoxyglucose |
| 18F-FDG | 18F-fluorodeoxyglucose |
| GBDT | Gradient-Boosted Decision Tree |
| GDPR | General Data Protection Regulation |
| GLOBOCAN | Global Cancer Observatory |
| IRB | Institutional Review Board |
| KNN | K-Nearest Neighbors |
| LASSO | Least Absolute Shrinkage and Selection Operator |
| LN | Lymph Node |
| LNM | Lymph Node Metastasis |
| LR | Logistic Regression |
| ML | Machine Learning |
| MLP | Multilayer Perceptron |
| MRI | Magnetic Resonance Imaging |
| NSCLC | Non-Small Cell Lung Cancer |
| PET | Positron Emission Tomography |
| PET/CT | Positron Emission Tomography/Computed Tomography |
| PICO | Population, Index Test, Comparator, Outcome |
| PRISMA | Preferred Reporting Items for Systematic Reviews and Meta-Analyses |
| QUADAS-2 | Quality Assessment of Diagnostic Accuracy Studies-2 |
| RF | Random Forest |
| SHAP | SHapley Additive exPlanations |
| SUV | Standardized Uptake Value |
| SUVmax | Maximum Standardized Uptake Value |
| SVM | Support Vector Machine |
| TBNA | Transbronchial Needle Aspiration |
| TCIA | The Cancer Imaging Archive |
| TLPC | Tumor and Lymph Node PET/CT |