Article Figures & Data
Figures
- FIG 1.
Flow chart of this study. RA indicates ruptured aneurysm; URA, Unruptured aneurysms; CFD, computational fluid dynamics.
- FIG 2.
Performance of the SVM algorithm, the derived top 10 variables, and the performances of feature—dependent models in the internal validation dataset. A, ROC curves and AUCs for training and internal and external validation sets. B, The top 10 features of the variables derived from the SVM algorithm. C, ROCs of the SVM based on the features belonging to the 3 categories separately in the internal validation dataset.
Tables
Performance of SVM to predict rupture status of small aneurysms in the training, internal validation, and external validation datasets
Training Set (n = 410) Internal Validation Set (n = 94) External Validation Set (n = 52) Tianjin Set (n = 30) Taizhou Set (n = 22) AUC 0.88 0.91 0.82 0.71 0.90 95% CI 0.85–0.92 0.74–0.98 0.69–0.94 0.52–0.86 0.70–0.99 Sensitivity 73.4% 77.3% 68.2% 54.5% 81.8% Specificity 91.1% 84.2% 76.7% 73.7% 81.8% Delong test – – .21a – .15b Note:—CI indicates confidence interval; LR, logistic regression; SVM, support vector machine; RF, random forest; ROC, receiver operation characteristic; RF, random forest; -, NA.
↵a P < . 05 means a significant difference exists in AUCs of SVM in the internal and external validation datasets.
↵b P < . 05 means a significant difference exists in AUCs of SVM in Taizhou and Tianjin sets.
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