. 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 7.

Texture Analysis Studies for Breast Cancer Treatment Response Prediction

Study	Study size	Acquisition	Texture features	Findings
Teruel et al⁶²	58	T1-weighted DCE-MRI	GLCM texture features	Entropy and sum variance were most significant in predicting stable disease vs. complete responders (AUC = 0.77) and predicting pCR (AUC = 0.69), respectively
Thibault et al⁶³	38	DCE-MRI	GLCM, run length features extracted from pharmacokinetic maps	GLCM features most predictive of therapy response
Golden et al⁶⁴	60	DCE-MRI	GLCM texture features extracted from pharmacokinetic maps	Pretherapy features can predict pCR and residual lymph node metastasis
Parikh et al⁶⁵	36	T2-and T1-weighted DCE-MRI	Entropy and uniformity	Responders to NACT showed increase in lesion homogeneity after one round of therapy
Michoux et al⁶⁶	69	T1-weighted DCE-MRI	GLCM, run-length	Model with three texture features and one kinetic feature identified nonresponders to NAC with 84% sensitivity
Braman et al³¹	117	T1-weighted DCE-MRI	Gabor, GLCM, Laws energy measures	Intratumor and peritumor texture features predicted pCR with AUC = 0.78