Table 6. Comparison of our multi-channel and multi-task deep learning (MMDL) method with the state of the art methods.
| Cancer type | Number of patients | Images | Extracted features | AUC | ACC (%) | SEN (%) | SPE (%) | ||
|---|---|---|---|---|---|---|---|---|---|
| A | Coudray (1) | NSCLC | 567 | Pathological | Deep features | 0.754 | – | – | – |
| B | Wang (26) | LUAD | 844 | CT | Deep features | 0.810 | 73.86 | 72.27 | 75.41 |
| C | Xiong (27) | LUAD | 158 | CT | Deep features, clinical features | 0.838 | 77.20 | 75.80 | 79.10 |
| D | Li (28) | LUAD | 1,010 | CT | Deep features, clinical features, radiomics | 0.834 | 82.20 | 74.20 | |
| E | Velazquez (10) | LUAD | 258 | CT | Radiomics, semantic features | 0.670 | – | – | – |
| F | Liu (9) | LUAD | 288 | CT | Clinical features | 0.709 | – | – | – |
| G | Li (21) | NSCLC | 312 | CT | Radiomics, clinical features | 0.775 | – | – | |
| H | Zhang (12) | NSCLC | 180 | CT | Radiomics | 0.873 | 75.60 | 70.90 | 79.80 |
| I | Gevaert (20) | NSCLC | 186 | CT | Semantic features | 0.890 | – | – | – |
| J | Guan (14) | NSCLC | 85 | PET/CT | SUVmax, clinical features | 0.770 | 77.60 | 64.60 | 82.50 |
| Our methods | NSCLC | 363 | CT | Deep features, clinical features | 0.866 | 79.43 | 78.27 | 81.35 |
LUAD, lung adenocarcinoma; NSCLC, non-small cell lung cancer; EGFR, epidermal growth factor receptor; KRAS, Kirsten rat sarcoma; AUC, area under the curve; sensitivity; ACC, accuracy; SEN, sensitivity; SPE, specificity.