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Proceedings Paper

Dynamic contrast-enhanced magnetic resonance imaging for prediction of response to neoadjuvant chemotherapy in breast cancer
Author(s): Juzhong Fu; Ming Fan; Bin Zheng; Guoliang Shao; Juan Zhang; Lihua Li
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Paper Abstract

Breast cancer is the second leading cause of women death in the United States. Currently, Neoadjuvant Chemotherapy (NAC) has become standard treatment paradigms for breast cancer patients. Therefore, it is important to find a reliable non-invasive assessment and prediction method which can evaluate and predict the response of NAC on breast cancer. The Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) approach can reflect dynamic distribution of contrast agent in tumor vessels, providing important basis for clinical diagnosis. In this study, the efficacy of DCE-MRI on evaluation and prediction of response to NAC in breast cancer was investigated. To this end, fifty-seven cases of malignant breast cancers with MRI examination both before and after two cycle of NAC were analyzed. After pre-processing approach for segmenting breast lesions and background regions, 126-dimensional imaging features were extracted from DCE-MRI. Statistical analyses were then performed to evaluate the associations between the extracted DCE-MRI features and the response to NAC. Specifically, pairwise t test was used to calculate differences of imaging features between MRI examinations before-and-after NAC. Moreover, the associations of these image features with response to NAC were assessed using logistic regression. Significant association are found between response to NAC and the features of lesion morphology and background parenchymal enhancement, especially the feature of background enhancement in normal side of breast (P=0.011). Our study indicate that DCE-MRI features can provide candidate imaging markers to predict response of NAC in breast cancer.

Paper Details

Date Published: 25 March 2016
PDF: 8 pages
Proc. SPIE 9789, Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations, 978905 (25 March 2016); doi: 10.1117/12.2217660
Show Author Affiliations
Juzhong Fu, Hangzhou Dianzi Univ. (China)
Ming Fan, Hangzhou Dianzi Univ. (China)
Bin Zheng, Hangzhou Dianzi Univ. (China)
The Univ. of Oklahoma (United States)
Guoliang Shao, Zhejiang Cancer Hospital (China)
Juan Zhang, 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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