Diffusion-weighted imaging features of breast tumours and the surrounding stroma reflect intrinsic heterogeneous characteristics of molecular subtypes in breast cancer
KAUST DepartmentComputational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Online Publication Date2017-12-15
Print Publication Date2018-02
Permanent link to this recordhttp://hdl.handle.net/10754/626388
MetadataShow full item record
AbstractBreast 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.
CitationFan M, He T, Zhang P, Cheng H, Zhang J, et al. (2017) Diffusion-weighted imaging features of breast tumours and the surrounding stroma reflect intrinsic heterogeneous characteristics of molecular subtypes in breast cancer. NMR in Biomedicine: e3869. Available: http://dx.doi.org/10.1002/nbm.3869.
SponsorsThis work was supported in part by funding from the National Natural Science Foundation of China (61401131, 61731008 and 61271063), the Natural Science Foundation of Zhejiang Province of China (LZ15F010001) and the National Basic Research Program of China (973 Program) (2013CB329502).
JournalNMR in Biomedicine
- Heterogeneity of Diffusion-Weighted Imaging in Tumours and the Surrounding Stroma for Prediction of Ki-67 Proliferation Status in Breast Cancer.
- Authors: Fan M, He T, Zhang P, Zhang J, Li L
- Issue date: 2017 Jun 6
- Histogram Analysis and Visual Heterogeneity of Diffusion-Weighted Imaging with Apparent Diffusion Coefficient Mapping in the Prediction of Molecular Subtypes of Invasive Breast Cancers.
- Authors: Horvat JV, Iyer A, Morris EA, Apte A, Bernard-Davila B, Martinez DF, Leithner D, Sutton OM, Ochoa-Albiztegui RE, Giri D, Pinker K, Thakur SB
- Issue date: 2019
- Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients.
- Authors: Tahmassebi A, Wengert GJ, Helbich TH, Bago-Horvath Z, Alaei S, Bartsch R, Dubsky P, Baltzer P, Clauser P, Kapetas P, Morris EA, Meyer-Baese A, Pinker K
- Issue date: 2019 Feb
- Relationship between functional imaging and immunohistochemical markers and prediction of breast cancer subtype: a PET/MRI study.
- Authors: Incoronato M, Grimaldi AM, Cavaliere C, Inglese M, Mirabelli P, Monti S, Ferbo U, Nicolai E, Soricelli A, Catalano OA, Aiello M, Salvatore M
- Issue date: 2018 Sep
- Characterization of breast masses as benign or malignant at 3.0T MRI with whole-lesion histogram analysis of the apparent diffusion coefficient.
- Authors: Suo S, Zhang K, Cao M, Suo X, Hua J, Geng X, Chen J, Zhuang Z, Ji X, Lu Q, Wang H, Xu J
- Issue date: 2016 Apr