Diffusion ‐weighted imaging features of breast tumours and the surrounding stroma reflect intrinsic heterogeneous characteristics of molecular subtypes in breast cancer

Breast cancer heterogeneity is the main obstacle preventing the identification of patients with breast cancer with poor prognoses and treatment responses; however, such heterogeneity has not been well characterized. The purpose of this retrospective study was to reveal heterogeneous patterns in the apparent diffusion coefficient (ADC) signals in tumours and the surrounding stroma to predict molecular subtypes of breast cancer. A dataset of 126 patients with breast cancer, who underwent preoperative diffusion‐weighted imaging (DWI) on a 3.0‐T image system, was collected. Breast images were segmented into regions comprising the tumour and surrounding stromal shells in which features that reflect heterogeneous ADC signal distribution were extracted. For each region, imaging features were computed, including the mean, minimum, variance, interquartile range (IQR), range, skewness, kurtosis and entropy of ADC values. Univariate and stepwise multivariate logistic regression modelling was performed to identify the magnetic resonance imaging features that optimally discriminate luminal A, luminal B, human epidermal growth factor 2 (HER2)‐enriched and basal‐like molecular subtypes. The performance of the predictive models was evaluated using the area under the receiver operating characteristic curve (AUC). Univariate logistic regression analysis showed that the skewness in the tumour boundary achieved an AUC of 0.718 for discrimination between luminal A and non‐luminal A tumo...
Source: NMR in Biomedicine - Category: Radiology Authors: Tags: RESEARCH ARTICLE Source Type: research