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©The Author(s) 2026.
World J Radiol. Jan 28, 2026; 18(1): 115504
Published online Jan 28, 2026. doi: 10.4329/wjr.v18.i1.115504
Published online Jan 28, 2026. doi: 10.4329/wjr.v18.i1.115504
Table 1 Clinical characteristics of patients in the training and testing cohorts, n (%)
| Characteristic | Training cohort (n = 225) | Testing cohort (n = 97) | ||||
| Expander (n = 83, 36.9%) | Non-expander (n = 142, 63.1%) | P value | Expander (n = 35, 36.1%) | Non-expander (n = 62, 63.9%) | P value | |
| Demographic characteristics | ||||||
| Age, median (IQR), years | 61 (28) | 61 (24) | 0.651 | 62 (31) | 59 (29) | 0.276 |
| Gender, male | 49 (59.0) | 81 (57.0) | 0.77 | 28 (80.0) | 33 (53.2) | 0.1 |
| Clinical features | ||||||
| Time to arrival, median (IQR), hour | 1.6 (0.7) | 1.5 (0.3) | 0.092 | 1.4 (0.6) | 1.6 (0.3) | 0.363 |
| Time to baseline CT, median (IQR), hour | 2.4 (1.1) | 2.2 (0.7) | 0.101 | 1.8 (0.7) | 1.9 (1.3) | 0.925 |
| Systolic BP, median (IQR), mmHg | 146 (40) | 147 (48) | 0.296 | 142 (39) | 144 (48) | 0.976 |
| Diastolic BP, median (IQR), mmHg | 86 (25) | 86 (27) | 0.974 | 82 (27) | 85 (22) | 0.905 |
| Heart rate, median (IQR), bpm | 80 (18) | 80 (18) | 0.988 | 84 (24) | 80 (20) | 0.86 |
| GCS score, median (IQR) | 13 (4) | 13 (5) | 0.634 | 13 (3) | 14 (3) | 0.847 |
| NIHSS score, median (IQR) | 6 (14) | 6 (12) | 0.866 | 5 (11) | 7 (11) | 0.754 |
| Medical history | ||||||
| Hypertension | 43 (51.8) | 67 (47.2) | 0.503 | 18 (51.4) | 26 (41.9) | 0.367 |
| Diabetes mellitus, male | 8 (9.6) | 14 (9.9) | 0.957 | 3 (8.6) | 6 (9.7) | 1 |
| Dyslipidemia | 3 (3.6) | 3 (2.1) | 0.672 | 1 (2.9) | 1 (1.6) | 1 |
| Atrial fibrillation | 3 (3.6) | 2 (1.4) | 0.361 | 1 (2.9) | 0 (0.0) | 0.361 |
| Acute coronary syndrome | 3 (3.6) | 4 (2.8) | 0.711 | 0 (0.0) | 4 (6.5) | 0.293 |
| Ischemic stroke | 0 (0.0) | 2 (1.4) | 0.532 | 1 (2.9) | 1 (1.6) | 1 |
| Current smoking | 6 (7.2) | 7 (4.9) | 0.557 | 2 (5.7) | 2 (3.2) | 0.618 |
| Drinking history | 2 (2.4) | 4 (2.8) | 1 | 0 (0.0) | 0 (0.0) | 1 |
| Medication history | ||||||
| Anti-platelet therapy | 6 (7.2) | 5 (3.5) | 0.22 | 1 (2.9) | 2 (3.2) | 1 |
| Anti-coagulant therapy | 7 (8.4) | 4 (2.8) | 0.104 | 3 (8.6) | 1 (1.6) | 0.132 |
Table 2 Predictive performance of radiological models, clinical model and clinical-radiological model in prediction of early enlargement of spontaneous intracerebral hemorrhage on patients in the testing cohort
| Model | ROI | AUC (95%CI) | Accuracy | Sensitivity | Specificity | PPV | NPV | F1 score | Brier score |
| Xception | Hemorrhage | 0.638 (0.526-0.750) | 61.86 | 68.57 | 58.06 | 48.00 | 76.60 | 0.565 | 0.507 |
| Perihematomal edema | 0.641 (0.529-0.753) | 62.89 | 65.71 | 61.29 | 48.94 | 76.00 | 0.561 | 0.535 | |
| Combined | 0.648 (0.535-0.761) | 61.86 | 54.29 | 66.13 | 47.50 | 71.93 | 0.507 | 0.865 | |
| VGG16 | Hemorrhage | 0.647 (0.535-0.759) | 63.92 | 34.29 | 80.65 | 50.00 | 68.49 | 0.407 | 0.517 |
| Perihematomal edema | 0.607 (0.501-0.720) | 57.73 | 45.71 | 64.52 | 42.11 | 67.80 | 0.438 | 0.442 | |
| Combined | 0.679 (0.567-0.791) | 59.79 | 88.57 | 43.55 | 46.97 | 87.10 | 0.614 | 0.519 | |
| VGG19 | Hemorrhage | 0.571 (0.500-0.683) | 58.76 | 68.57 | 53.23 | 45.28 | 75.00 | 0.545 | 0.402 |
| Perihematomal edema | 0.667 (0.555-0.779) | 60.82 | 80.00 | 50.00 | 47.46 | 81.58 | 0.596 | 0.523 | |
| Combined | 0.725 (0.613-0.837) | 68.04 | 80.00 | 61.29 | 53.85 | 84.44 | 0.644 | 0.551 | |
| ResNet50 | Hemorrhage | 0.747 (0.636-0.858) | 63.92 | 82.86 | 53.23 | 50.00 | 84.62 | 0.624 | 0.686 |
| Perihematomal edema | 0.659 (0.547-0.771) | 46.39 | 71.43 | 32.26 | 37.31 | 66.67 | 0.490 | 0.575 | |
| Combined | 0.774 (0.662-0.886) | 60.82 | 88.57 | 45.16 | 47.69 | 87.50 | 0.620 | 0.703 | |
| InceptionV3 | Hemorrhage | 0.620 (0.511-0.729) | 55.67 | 74.29 | 45.16 | 43.33 | 75.68 | 0.547 | 0.491 |
| Perihematomal edema | 0.637 (0.528-0.746) | 56.70 | 65.71 | 51.61 | 43.40 | 72.73 | 0.523 | 0.549 | |
| Combined | 0.704 (0.592-0.816) | 62.89 | 68.57 | 59.68 | 48.98 | 77.08 | 0.571 | 0.587 | |
| InceptionResNetV2 | Hemorrhage | 0.663 (0.552-0.774) | 69.07 | 54.29 | 77.42 | 57.58 | 75.00 | 0.559 | 0.549 |
| Perihematomal edema | 0.683 (0.571-0.795) | 53.61 | 77.14 | 40.32 | 42.19 | 75.76 | 0.545 | 0.628 | |
| Combined | 0.728 (0.615-0.841) | 64.95 | 82.86 | 54.84 | 50.88 | 85.00 | 0.630 | 0.588 | |
| Handcrafted radiomics | Hemorrhage | 0.564 (0.502-0.626) | 53.61 | 40.00 | 61.29 | 36.84 | 64.41 | 0.384 | 0.446 |
| Perihematomal edema | 0.507 (0.500-0.563) | 55.67 | 68.57 | 48.39 | 42.86 | 73.17 | 0.527 | 0.358 | |
| Combined | 0.635 (0.527-0.743) | 54.64 | 62.86 | 50.00 | 41.51 | 70.45 | 0.500 | 0.579 | |
| Radiological model | - | 0.713 (0.600-0.826) | 72.16 | 60.00 | 79.03 | 61.76 | 77.78 | 0.609 | 0.576 |
| Clinical model | - | 0.534 (0.501-0.567) | 55.67 | 45.71 | 61.29 | 40.00 | 66.67 | 0.427 | 0.414 |
| Integrated model | - | 0.828 (0.714-0.942) | 72.89 | 70.00 | 74.52 | 58.84 | 84.07 | 0.638 | 0.749 |
Table 3 Predictive performance of radiological models, clinical model and clinical-radiological model in prediction of hospital death on patients in the testing cohort
| Model | ROI | AUC (95%CI) | Accuracy | Sensitivity | Specificity | PPV | NPV | F1 score | Brier score |
| Xception | Hemorrhage | 0.517 (0.501-0.533) | 70.10 | 50.00 | 72.41 | 17.24 | 92.65 | 0.256 | 0.132 |
| Perihematomal edema | 0.513 (0.508-0.520) | 64.95 | 30.00 | 68.97 | 10.00 | 89.55 | 0.150 | 0.103 | |
| Combined | 0.549 (0.510-0.588) | 76.29 | 30.00 | 81.61 | 15.79 | 91.03 | 0.207 | 0.153 | |
| VGG16 | Hemorrhage | 0.580 (0.528-0.632) | 57.73 | 40.00 | 59.77 | 10.26 | 89.66 | 0.163 | 0.249 |
| Perihematomal edema | 0.584 (0.523-0.645) | 57.73 | 20.00 | 62.07 | 5.71 | 87.10 | 0.089 | 0.092 | |
| Combined | 0.607 (0.532-0.682) | 75.26 | 30.00 | 80.46 | 15.00 | 90.91 | 0.200 | 0.154 | |
| VGG19 | Hemorrhage | 0.586 (0.555-0.617) | 63.92 | 30.00 | 67.82 | 89.68 | 89.39 | 0.146 | 0.099 |
| Perihematomal edema | 0.606 (0.564-0.648) | 67.01 | 10.00 | 73.56 | 4.17 | 87.67 | 0.059 | 0.092 | |
| Combined | 0.654 (0.567-0.741) | 68.04 | 20.00 | 73.56 | 8.00 | 88.89 | 0.114 | 0.106 | |
| ResNet50 | Hemorrhage | 0.570 (0.510-0.630) | 67.01 | 40.00 | 70.11 | 13.33 | 91.04 | 0.200 | 0.173 |
| Perihematomal edema | 0.561 (0.512-0.610) | 71.13 | 50.00 | 73.56 | 17.86 | 92.75 | 0.263 | 0.149 | |
| Combined | 0.705 (0.606-0.804) | 64.95 | 60.00 | 65.52 | 16.67 | 93.44 | 0.261 | 0.254 | |
| InceptionV3 | Hemorrhage | 0.508 (0.500-0.517) | 72.16 | 40.00 | 75.86 | 16.00 | 91.67 | 0.229 | 0.309 |
| Perihematomal edema | 0.517 (0.509-0.525) | 62.89 | 40.00 | 65.52 | 11.76 | 90.48 | 0.182 | 0.120 | |
| Combined | 0.547 (0.516-0.578) | 68.04 | 50.00 | 70.11 | 16.13 | 92.42 | 0.244 | 0.142 | |
| InceptionResNetV2 | Hemorrhage | 0.502 (0.500-0.508) | 65.98 | 30.00 | 70.11 | 10.34 | 89.71 | 0.154 | 0.136 |
| Perihematomal edema | 0.597 (0.535-0.659) | 72.16 | 40.00 | 75.86 | 16.00 | 91.67 | 0.229 | 0.262 | |
| Combined | 0.615 (0.540-0.690) | 70.10 | 60.00 | 71.26 | 19.35 | 93.94 | 0.293 | 0.189 | |
| Handcrafted radiomics | Hemorrhage | 0.600 (0.546-0.654) | 70.10 | 60.00 | 71.26 | 19.35 | 93.94 | 0.293 | 0.223 |
| Perihematomal edema | 0.516 (0.505-0.527) | 57.73 | 30.00 | 60.92 | 8.11 | 88.33 | 0.128 | 0.127 | |
| Combined | 0.603 (0.534-0.672) | 67.01 | 50.00 | 68.97 | 15.63 | 92.31 | 0.238 | 0.168 | |
| Hematoma expansion | - | 0.546 (0.524-0.568) | 65.98 | 30.00 | 70.11 | 10.34 | 89.71 | 0.154 | 0.158 |
| Radiological model | - | 0.655 (0.576-0.734) | 67.01 | 30.00 | 71.26 | 10.71 | 89.86 | 0.158 | 0.208 |
| Integrated model | - | 0.754 (0.646-0.862) | 71.13 | 60.00 | 72.41 | 20.00 | 94.03 | 0.300 | 0.241 |
- Citation: Yang YH, Li Y. Deep learning-based imaging model to predict early hematoma enlargement and hospital mortality in spontaneous intracerebral hemorrhage. World J Radiol 2026; 18(1): 115504
- URL: https://www.wjgnet.com/1949-8470/full/v18/i1/115504.htm
- DOI: https://dx.doi.org/10.4329/wjr.v18.i1.115504
