FIGURE 1.
Figure 2.
Figure 3.
Figure 4.
Figure 5.
Distribution of conventional CT features in training and testing dataset
| Testing set | Training set | |||||
|---|---|---|---|---|---|---|
| Low-risk (n=32) | High-risk (n= 72) | P | Low-risk (n=14) | High-risk (n=31) | P | |
| Mean CT value (HU) | 79.5 (68.4, 91.6) | 62.0 (51.0, 78.8) | <0.001 | 88.5 (76.0, 95.1) | 67.0 (59.2, 74.0) | <0.001 |
| Standard deviation | 18.0 (16.0, 22.6) | 16.5 (14.0, 19.0) | 0.050 | 18.0 (15.9, 26.2) | 17.0 (14.2, 21.6) | 0.548 |
| Minimum CT value (HU) | -6.10±30.2 | -8.5±25.0 | 0.678 | -0.7±31.6 | -7.3±17.9 | 0.477 |
| Maximum CT value (HU) | 148.5 (131.0, 172.1) | 118.0 (105.5, 138.6) | <0.001 | 162.0 (148.9, 166.1) | 129.0 (105.4, 146.8) | 0.002 |
| Long diameter (mm) | 50.6±17.0 | 44.1±19.4 | 0.106 | 47.8 (40.9, 57.8) | 38.0 (27.7, 61.3) | 0.198 |
| Short diameter (mm) | 34.7 (23.9, 41.7) | 23.2 (17.7, 34.6) | 0.009 | 36.5 (26.0, 45.4) | 25.6 (19.0, 39.3) | 0.073 |
| Vertical diameter (mm) | 48.6 (44.1, 60.2) | 40.4 (29.1, 55.2) | 0.204 | 50.5 (44.4, 63.6) | 38.9 (33.1, 55.1) | 0.059 |
| Area (mm2) | 1321.5 (692.0, 1889.8) | 628.5 (397.4, 1409.7) | 0.008 | 1024.0 (747.7, 1623.3) | 651.0 (346.0, 1362.8) | 0.315 |
| Perimeter (mm) | 143.0 (110.8, 167.7) | 112.5 (78.6, 153.8) | 0.021 | 143.5 (118.6, 255.4) | 100.0 (84.1, 194.8) | 0.098 |
| Location | 0.373 | 0.790 | ||||
| Right mediastinum | 10 (31.3%) | 33 (45.8%) | 7 (50.0%) | 11 (35.5%) | ||
| Middle | 8 (25.0%) | 15 (20.8%) | 1 (7.1%) | 3 (9.7%) | ||
| Left mediastinum | 14 (43.8%) | 24 (33.3%) | 6 (42.9%) | 17 (54.8%) | ||
| Morphology | 0.010 | <0.001 | ||||
| Lobular | 5 (15.6%) | 10 (13.9%) | 7 (50.0%) | 2 (6.5%) | ||
| Shallowly-lobulated | 15 (46.9%) | 14 (19.4%) | 7 (50.0%) | 15 (48.4%) | ||
| Non-lobular | 12 (37.5%) | 48 (66.7%) | 0 (0.0%) | 14 (45.2%) | ||
| Demarcation | 0.023 | 0.010 | ||||
| Clear | 15 (46.9%) | 17 (23.6%) | 10 (71.4%) | 8 (25.8%) | ||
| Unclear | 16 (50.0%) | 43 (59.7%) | 4 (28.6%) | 17 (54.8%) | ||
| Infiltration | 1 (3.1%) | 12 (16.7%) | 0 (0%) | 6 (19.4%) | ||
| Internal calcification | 8 (25.0%) | 13 (18.1%) | 0.416 | 4 (28.6%) | 9 (29.0%) | 0.746 |
| Necrosis | 12 (37.5%) | 20 (27.8%) | 0.321 | 9 (64.3%) | 12 (38.7%) | 0.111 |
Baseline characteristics of the patients in training and testing dataset
| Training set | Testing set | |||||
|---|---|---|---|---|---|---|
| Low-risk (n=32) | High-risk (n= 72) | P | Low-risk (n=14) | High-risk (n=31) | P | |
| Age, (Mean ± SD) years | 53.6±11.2 | 52.5±11.4 | 0.656 | 54.0±10.7 | 56.4±8.9 | 0.446 |
| Sex (male, No. (%)) | 14 (43.8) | 37 (51.4) | 0.472 | 7 (50.0) | 18 (58.1) | 0.614 |
| Myasthenia gravis, No. (%) | 7 (21.9) | 24 (33.3) | 0.238 | 0 (0.0) | 8 (25.8) | 0.094 |
| Thoracalgia, No. (%) | 3 (9.4) | 18 (25.0) | 0.067 | 1 (7.1) | 11 (35.5) | 0.104 |
Diagnostic performance of the three models
| Model | Training dataset | Testing dataset | ||||
|---|---|---|---|---|---|---|
| Sensitivity | Specificity | AUC (95%CI) | Sensitivity | Specificity | AUC (95%CI) | |
| Conventional models | 77.8% | 87.5% | 0.863(0.786-0.940) | 54.8% | 100.0% | 0.853(0.740-0.965) |
| 2D radiomics model | 86.1% | 71.9% | 0.854(0.777-0.931) | 77.4% | 85.7% | 0.834(0.714-0.984) |
| 3D radiomics model | 75.0% | 93.8% | 0.902(0.842-0.963) | 67.7% | 100.0% | 0.906(0.820-0.991) |