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

Autor:	Velichko YS; Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois; Quantitative Imaging Core Lab, Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois. Electronic address: y-velichko@northwestern.edu., Mozafarykhamseh A; Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois., Trabzonlu TA; Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois., Zhang Z; Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois; Quantitative Imaging Core Lab, Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois., Rademaker AW; Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois., Yaghmai V; Quantitative Imaging Core Lab, Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois.
Jazyk:	angličtina
Zdroj:	Academic radiology [Acad Radiol] 2021 Apr; Vol. 28 (4), pp. e93-e100. Date of Electronic Publication: 2020 Apr 14.
DOI:	10.1016/j.acra.2020.03.004
Abstrakt:	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. (Copyright © 2020 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu