Fig 1.
Representative tumors with high and low values for each texture feature (A).
Schematic of prediction model of protein expression constructed from quantitative imaging phenotypes. Quantitative image phenotypes are derived via texture analysis: the tumor region is extracted from CT, texture feature statistics are automatically computed based on the region of interest (B).
Fig 2.
Selected linear regression plots of texture features with respect to protein expression levels.
The 95% confidence interval is rendered. For descriptive purpose, each protein is plotted against the texture feature that contributed the most to the prediction model (A). Two intrahepatic cholangiocarcinomas with low (top row) and high (bottom row) VEGF/EGFR protein expression by immunohistochemistry. After semi-automated segmentation of tumor borders from CT images, the tumor pixel attenuation values are evaluated for texture features. Axial slices of the segmented tumors are shown with texture features calculated for each slice and averaged. (B).
Table 1.
Multiple linear regression analysis of hypoxia markers and quantitative imaging phenotypes.
Table 2.
Relationship between qualitative imaging features and protein expression levels by linear regression.