Table 1.
Patient demographics.
Fig 1.
T1 post-contrast image (left) and T2 FLAIR image (right) after preprocessing.
Arrows indicate the enhanced tumor area.
Fig 2.
A representative 2D histogram of the ROI of a T1 post-contrast image.
Intensity values are binned into two groups based on pixel membership to a Gaussian mixture model. Solid red line represents each Gaussian population; dotted green line represents total intensity distribution of the ROI.
Fig 3.
A spatial map was combined with the low-intensity T1 post-contrast ROI, high-intensity T1 post-contrast ROI, low-intensity T2 FLAIR ROI, and high-intensity T2 FLAIR ROI. Each binary mask is equally divided into several bounding boxes and the size of each bounding box is 8×8 pixels. The coordinates of each centroid indicating the center of mass of each group inside of the small bounding grid box were calculated, and coordinates from all four regions were combined into a spatial map. The centroids of each subpopulation are designated by four different shapes based on group to illustrate the spatial relations between the four groups. Different gray levels of ROI represent each habitat and overlaps of habitats.
Fig 4.
This flowchart represents the global clustering steps for the spatial point pattern map.
Table 2.
List of features.
Table 3.
p-value of survival difference between feature-induced groups (from ROC analysis).
Table 4.
ROC analysis for subtypes (the TPR/TNR/ACC values were obtained at an operating point by maximizing the sum of sensitivity and specificity).
Fig 5.
ROC curve for the prediction of survival at the 12-month time point.
The x-axis is the false- positive rate, 1 –specificity; the y-axis is the true- positive rate, sensitivity. The area under the ROC curve is 0.76.