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dc.contributor.authorFan, Ming
dc.contributor.authorHe, Ting
dc.contributor.authorZhang, Peng
dc.contributor.authorCheng, Hu
dc.contributor.authorZhang, Juan
dc.contributor.authorGao, Xin
dc.contributor.authorLi, Lihua
dc.date.accessioned2017-12-17T13:48:01Z
dc.date.available2017-12-17T13:48:01Z
dc.date.issued2017-12-15
dc.identifier.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.
dc.identifier.issn0952-3480
dc.identifier.pmid29244222
dc.identifier.doi10.1002/nbm.3869
dc.identifier.urihttp://hdl.handle.net/10754/626388
dc.description.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.
dc.description.sponsorshipThis 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).
dc.publisherWiley
dc.relation.urlhttp://onlinelibrary.wiley.com/doi/10.1002/nbm.3869/abstract
dc.rightsThis is the peer reviewed version of the following article: Diffusion-weighted imaging features of breast tumours and the surrounding stroma reflect intrinsic heterogeneous characteristics of molecular subtypes in breast cancer, which has been published in final form at http://doi.org/10.1002/nbm.3869. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
dc.subjectBreast cancer
dc.subjectApparent diffusion coefficient
dc.subjectDiffusion-weighted Imaging
dc.subjectMolecular Subtype
dc.titleDiffusion-weighted imaging features of breast tumours and the surrounding stroma reflect intrinsic heterogeneous characteristics of molecular subtypes in breast cancer
dc.typeArticle
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalNMR in Biomedicine
dc.eprint.versionPost-print
dc.contributor.institutionInstitute of Biomedical Engineering and Instrumentation; Hangzhou Dianzi University; Hangzhou China
dc.contributor.institutionZhejiang Cancer Hospital; Zhejiang Hangzhou China
kaust.personGao, Xin
dc.date.published-online2017-12-15
dc.date.published-print2018-02


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