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

Abstract

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 tumours, whereas the IQR of the ADC value in the tumour boundary had an AUC of 0.703 for classification of the HER2-enriched subtype. Imaging features in the tumour boundary and the proximal peritumoral stroma corresponded to a higher overall prediction performance than those in other regions. A multivariate logistic regression model combining features in all the regions achieved an overall AUC of 0.800 for the classification of the four tumour subtypes. These findings suggest that features in the tumour boundary and stroma around the tumour may be further assessed as potential predictors of molecular subtypes of breast cancer.

Quantitative Multiparametric MRI as an Imaging Biomarker for the Prediction of Breast Cancer Receptor Status and Molecular Subtypes

“…This phenomenon may be attributed to the high cellular density and the restriction of water molecules in malignant tumors (30). A recent study further revealed that the features of DWI could reflect the intrinsic heterogeneous characteristics of molecular subtypes in BC (31). Our study further quantified the ADC derived from RS-EPI DWI and demonstrated that the ADC value was statistically significant in identifying HER-2-enriched BC and TNBC with an AUC of 0.763 (95% CI: 0.681-0.904).…”

Perfusion-driven Intravoxel Incoherent Motion (IVIM) MRI in Oncology: Applications, Challenges, and Future Trends

“…40 Lower ADC values in estrogen receptor (ER) or progesterone receptor (PgR) positive tumors have already been reported in many papers. [42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61] Kawashima et al 53 demonstrated significantly lower D and ADC values in luminal B compared with luminal A tumors. Iima et al 58 reported significantly lower sADC 200-1500 values in PgR expression.…”

Joint Prediction of Breast Cancer Histological Grade and Ki-67 Expression Level Based on DCE-MRI and DWI Radiomics

“…However, to predict tumor characteristics, only a single parametric MRI is usually used. For example, studies indicate that radiomic analysis of MRI may be helpful in predicting breast cancer subtypes [13][14][15][16], neoadjuvant chemotherapy responses [17][18][19], Ki-67 expression level [20][21][22], pathological stage [23], histological grade [24], tumor malignancy [25,26], and breast cancer risk [27] based on either DCE-MRI or DWI. These studies were performed without considering complementary information that could be provided by different parametric images.…”