Article Figures & Data
- Figure 1
The concept of the model in this study: (1) the model had a single input (CT imaging data) and two outputs for segmentation and prediction; (2) based on the U architecture, the high-level image information derived from the bridge layer of U were treated as biomarkers for haematoma expansion prediction.
- Figure 2
Flow chart illustrating patient selection for training dataset and testing dataset.
- Figure 3
An illustrative case of the segmentation result: the haematoma segmented by the convolutional neural networks (CNN) model was in green, and the segmentation in the manual method was in red.
- Table 1
Patientcharacteristics grouped by training and testing datasets
Variable name (and type) Training dataset Testing dataset P value* Sample size (n) 189 77 Age, years, mean±SD 62.2±13.4 63.3±12.0 0.521 Sex, male, n (%) 132 (69.8) 54 (70.1) 0.963 Hypertension, n (%) 140 (74.9) 62 (80.5) 0.325 Diabetes mellitus, n (%) 19 (10.2) 17 (22.1) 0.010 Prestroke, n (%) 13 (6.9) 11 (14.3) 0.056 Antiplatelet history, n (%) 13 (6.9) 7 (9.1) 0.535 Anticoagulation history, n 2† 0 N/A Time to baseline CT, hours, mean±SD 3.4±2.0 3.9±2.2 0.096 Baseline NIHSS score, median (IQR) 9 (5–12) 8 (4–14) 0.718 Baseline haematoma volume, mL, mean±SD 17.4±15.3 17.8±18.8 0.850 Intraventricular haemorrhage, n (%) 70 (37.0) 31 (40.3) 0.623 NCCT markers Hypodensities 132 (69.8) 44 (57.1) 0.047 Black hole sign 28 (14.8) 12 (15.6) 0.873 Swirl sign 116 (61.4) 44 (57.1) 0.523 Blend sign 24 (12.7) 13 (16.9) 0.371 Fluid level 13 (6.9) 9 (11.7) 0.196 Irregular shape 143 (75.7) 50 (64.9) 0.075 Heterogeneous density 100 (52.9) 34 (44.2) 0.195 BAT score, median (IQR) 2 (2–4) 2 (0–3) 0.065 Haematoma expansion, n (%) 52 (27.5) 22 (28.6) 0.861 *Continuous variables were compared using Mann-Whitney U test and Student’s t-test as appropriate and categorical variables were compared using Pearson’s χ2 test.
†Two patients with warfarin history for atrial fibrillation had no haematoma expansion.
NCCT, non-contrast CT; NIHSS, National Institute of Health Stroke Scale.
- Table 2
Scores for models and NCCT markers of testing dataset
Sensitivity Specificity Positive likelihood ratio* Negative likelihood ratio* AUC P value† Hypodensities 0.77
(0.54 to 0.91)0.51
(0.37 to 0.64)0.63
(0.40 to 0.98)0.18
(0.08 to 0.40)0.64
(0.53 to 0.75)0.026 Black hole sign 0.36
(0.18 to 0.59)0.93
(0.82 to 0.98)2.00
(0.82 to 4.89)0.27
(0.17 to 0.44)0.65
(0.54 to 0.75)0.006 Swirl sign 0.86
(0.64 to 0.96)0.54
(0.41 to 0.68)0.76
(0.50 to 1.16)0.1
(0.03 to 0.30)0.70
(0.61 to 0.80)0.211 Blend sign 0.18
(0.06 to 0.41)0.84
(0.71 to 0.92)0.44
(0.18 to 1.08)0.39
(0.26 to 0.59)0.51
(0.41 to 0.61)<0.001 Fluid level 0.23
(0.09 to 0.46)0.93
(0.82 to 0.98)1.25
(0.49 to 3.19)0.33
(0.22 to 0.51)0.58
(0.48 to 0.67)0.002 Irregular shape 0.73
(0.50 to 0.88)0.38
(0.261 to 0.52)0.47
(0.30 to 0.74)0.29
(0.14 to 0.59)0.55
(0.44 to 0.67)0.002 Heterogeneous density 0.73
(0.50 to 0.88)0.67
(0.53 to 0.79)0.89
(0.55 to 1.43)0.16
(0.08 to 0.34)0.70
(0.59 to 0.81)0.141 Haematoma Volume 0.50
(0.29to 0.71)0.84
(0.71 to 0.92)1.22
(0.65 to 2.29)0.24
(0.14 to 0.41)0.62
(0.46 to 0.78)0.042 BAT score 0.36
(0.18to 0.59)0.76
(0.62 to 0.86)0.61
(0.32 to 1.17)0.33
(0.21 to 0.53)0.65
(0.53 to 0.78)0.042 CNN 0.91
(0.69to 0.98)0.58
(0.44 to 0.71)0.87
(0.57 to 1.33)0.06
(0.02 to 0.24)0.80
(0.70 to 0.90)N/A *Positive likelihood ratio and negative likelihood ratio were weighted by prevalence.
†AUC of CNN model was compared with AUC of the other models and NCCT signs using Delong test.
AUC, receiver operator characteristic area under the curve; CNN, convolutional neural network; N/A, not applicable; NCCT, non-contrast CT.
Supplementary Materials
Supplementary data
Additional Files
Supplementary Data
This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.
Supplementary Data
This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.
