Association Between the Size and 3D CT-Based Radiomic Features of Breast Cancer Hepatic Metastasis

Yuri S. Velichko*, Amirhossein Mozafarykhamseh, Tugce Agirlar Trabzonlu, Zhuoli Zhang, Alfred W. Rademaker, Vahid Yaghmai

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
JournalAcademic radiology
DOIs
StateAccepted/In press - Jan 1 2020

Keywords

  • Computed tomography
  • Features
  • Gray levels
  • Radiomics
  • Texture
  • Tumor size

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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