. Author manuscript; available in PMC: 2020 Mar 17.

Published in final edited form as: J Magn Reson Imaging. 2018 Nov 3;49(4):927–938. doi: 10.1002/jmri.26556

TABLE 5.

Texture Analysis Studies for Breast Cancer Histopathologic and Molecular Subtype Classification

Study	Study size	Acquisition	Texture features	Findings
Holli-Helenius et al⁵¹	27	T1-weighted, nonfat-saturated DCE-MRI	GLCM texture features	Sum entropy and sum variance differentiate between luminal A and luminal B subtypes (AUC = 0.88)
Waugh et al⁵⁰	200	DCE-MRI	GLCM texture features	Entropy significantly different between ILC and IDC cancers
Sutton et al⁵²	178	T1-weighted fat suppressed MRI	Gray-level histogram, GLCM texture features	Features quantifying heterogeneity were able to classify between molecular subtypes
Wang et al³²	84	DCE-MRI	Gray-level histogram, GLCM texture features	Adding texture features quantifying tumor microenvironment heterogeneity to model with features quantifying lesion heterogeneity improved classification performance to identify TNBC.
Chen et al⁴⁸	121	T1-weighted DCE-MRI	GLCM texture features	3D texture features showed better performance than 2D texture features when classifying breast lesions as benign or malignant