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Proceedings Paper

Radiogenomic analysis of breast cancer: dynamic contrast enhanced - magnetic resonance imaging based features are associated with molecular subtypes
Author(s): Shijian Wang; Ming Fan; Juan Zhang; Bin Zheng; Xiaojia Wang; Lihua Li
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Paper Abstract

Breast cancer is one of the most common malignant tumor with upgrading incidence in females. The key to decrease the mortality is early diagnosis and reasonable treatment. Molecular classification could provide better insights into patient-directed therapy and prognosis prediction of breast cancer. It is known that different molecular subtypes have different characteristics in magnetic resonance imaging (MRI) examination. Therefore, we assumed that imaging features can reflect molecular information in breast cancer. In this study, we investigated associations between dynamic contrasts enhanced MRI (DCE-MRI) features and molecular subtypes in breast cancer. Sixty patients with breast cancer were enrolled and the MR images were pre-processed for noise reduction, registration and segmentation. Sixty-five dimensional imaging features including statistical characteristics, morphology, texture and dynamic enhancement in breast lesion and background regions were semiautomatically extracted. The associations between imaging features and molecular subtypes were assessed by using statistical analyses, including univariate logistic regression and multivariate logistic regression. The results of multivariate regression showed that imaging features are significantly associated with molecular subtypes of Luminal A (p=0.00473), HER2-enriched (p=0.00277) and Basal like (p=0.0117), respectively. The results indicated that three molecular subtypes are correlated with DCE-MRI features in breast cancer. Specifically, patients with a higher level of compactness or lower level of skewness in breast lesion are more likely to be Luminal A subtype. Besides, the higher value of the dynamic enhancement at T1 time in normal side reflect higher possibility of HER2-enriched subtype in breast cancer.

Paper Details

Date Published: 25 March 2016
PDF: 10 pages
Proc. SPIE 9789, Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations, 978904 (25 March 2016); doi: 10.1117/12.2217658
Show Author Affiliations
Shijian Wang, Hangzhou Dianzi Univ. (China)
Ming Fan, Hangzhou Dianzi Univ. (China)
Juan Zhang, Zhejiang Cancer Hospital (China)
Bin Zheng, Hangzhou Dianzi Univ. (China)
The Univ. of Oklahoma (United States)
Xiaojia Wang, Zhejiang Cancer Hospital (China)
Lihua Li, Hangzhou Dianzi Univ. (China)

Published in SPIE Proceedings Vol. 9789:
Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations
Jianguo Zhang; Tessa S. Cook, Editor(s)

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