Open AccessFeatures from Computerized Texture Analysis of Breast Cancers at Pretreatment MR Imaging Are Associated with Response to Neoadjuvant Chemotherapy
Author(s) -
Foucauld Chamming’s,
Yoshiko Ueno,
Romuald Ferré,
Ellen Kao,
AnneSophie Jannot,
Jaron Chong,
Atilla Ömeroğlu,
Benoı̂t Mesurolle,
Caroline Reinhold,
B. Gallix
Publication year - 2018
Publication title -
radiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.118
H-Index - 295
eISSN - 1527-1315
pISSN - 0033-8419
DOI - 10.1148/radiol.2017170143
Subject(s) - medicine , breast cancer , magnetic resonance imaging , breast mri , univariate analysis , chemotherapy , logistic regression , neoadjuvant therapy , cancer , triple negative breast cancer , nuclear medicine , radiology , oncology , multivariate analysis , mammography
Purpose To evaluate whether features from texture analysis of breast cancers were associated with pathologic complete response (pCR) after neoadjuvant chemotherapy and to explore the association between texture features and tumor subtypes at pretreatment magnetic resonance (MR) imaging. Materials and Methods Institutional review board approval was obtained. This retrospective study included 85 patients with 85 breast cancers who underwent breast MR imaging before neoadjuvant chemotherapy between April 10, 2008, and March 12, 2015. Two-dimensional texture analysis was performed by using software at T2-weighted MR imaging and contrast material-enhanced T1-weighted MR imaging. Quantitative parameters were compared between patients with pCR and those with non-pCR and between patients with triple-negative breast cancer and those with non-triple-negative cancer. Multiple logistic regression analysis was used to determine independent parameters. Results Eighteen tumors (22%) were triple-negative breast cancers. pCR was achieved in 30 of the 85 tumors (35%). At univariate analysis, mean pixel intensity with spatial scaling factor (SSF) of 2 and 4 on T2-weighted images and kurtosis on contrast-enhanced T1-weighted images showed a significant difference between triple-negative breast cancer and non-triple-negative breast cancer (P = .009, .003, and .001, respectively). Kurtosis (SSF, 2) on T2-weighted images showed a significant difference between pCR and non-pCR (P = .015). At multiple logistic regression, kurtosis on T2-weighted images was independently associated with pCR in non-triple-negative breast cancer (P = .033). A multivariate model incorporating T2-weighted and contrast-enhanced T1-weighted kurtosis showed good performance for the identification of triple-negative breast cancer (area under the receiver operating characteristic curve, 0.834). Conclusion At pretreatment MR imaging, kurtosis appears to be associated with pCR to neoadjuvant chemotherapy in non-triple-negative breast cancer and may be a promising biomarker for the identification of triple-negative breast cancer. © RSNA, 2017.