Article Figures & Data
Figures
- Fig 1.
Visualized feature images of thrombus of 2 patients, one with early recanalization and another without recanalization. The selected feature, Laws SD of LSW, is demonstrated. The LSW value (gray-level) of each pixel in the feature map is measuring the heterogeneity in the local region of each pixel.
- Fig 2.
ROC results of the best radiomics features from different feature groups, compared with clinically relevant currently measurable features such as thrombus length, volume, and permeability measurements, as well as subjective assessment of the residual flow grade. The numbers in the parentheses following legend names denote AUC values and ranking after linear discriminative analysis.
- Fig 3.
ROC curves for the prediction of early recanalization with IV alteplase using 67 patients (30 patients versus 37 controls). The bars on the curves represent the SDs of 10 repetitions. A, ROC curves for each feature group. B, ROC curves for different combinations of feature groups.
Tables
Radiomics features used in the analysis
Method No. of Features Set 1: First-order statistics Voxel intensity distribution 34 Set 2: Shape and size Thrombus length, surface, and volume 6 Set 3: Textural features Gray-level co-occurrence matrix 78 Gray-level run length matrix 66 Neighborhood gray-level difference matrix 10 Law texture 105 Local binary pattern 27
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