UC Irvine

Purpose

To evaluate the effect of the anatomic size on 3D radiomic imaging features of the breast cancer hepatic metastases.

Materials and methods

CT scans of 81 liver metastases from 54 patients with breast cancer were evaluated. Ten most common 3D radiomic features from the histogram and gray level co-occurrence matrix (GLCM) categories were calculated for the hepatic metastases (HM) and compared to normal liver (NL). The effect of size was evaluated by using linear mixed-effects regression models. The effect of size on different radiomic features was analyzed for both liver lesions and background liver.

Results

Three-dimensional radiomic features from GLCM demonstrate an important size dependence. The texture-feature size dependence was found to be different among feature categories and between the HM and NL, thus demonstrating a discriminatory power for the tissue type. Significant difference in the slope was found for GLCM homogeneity (NL slope = 0.004, slope difference 95% confidence interval [CI] 0.06-0.1, p <0.001), contrast (NL slope = 45, slope difference 95% CI 205-305, p <0.001), correlation (NL slope = 0.04, slope difference 95% CI 0.11-0.21, p <0.001), and dissimilarity (NL slope = 0.7, slope difference 95% CI 3.6-5.4, p <0.001). The GLCM energy (NL slope = 0.002, slope difference 95% CI -0.0005 to -0.0003, p <0.007), and entropy (NL slope = 1.49, slope difference 95% CI 0.07-0.52, p <0.009) exhibited size-dependence for both NL and HM, although demonstrating a difference in the slope between themselves.

Conclusion

Radiomic features of breast cancer hepatic metastasis exhibited significant correlation with tumor size. This finding demonstrates the complex behavior of imaging features and the need to include feature-specific properties into radiomic models.