Prof. Lijuan Huang Profile

Prof. Lijuan Huang

at Hangzhou Dianzi Univ

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Proceedings Article | 25 March 2016 Paper

Association between dynamic features of breast DCE-MR imaging and clinical response of neoadjuvant chemotherapy: a preliminary analysis

Lijuan Huang, Ming Fan, Lihua Li, Juan Zhang, Guoliang Shao, Bin Zheng

Proceedings Volume 9789, 97890Z (2016) https://doi.org/10.1117/12.2225142

KEYWORDS: Magnetic resonance imaging, Breast cancer, Image segmentation, Breast, Feature extraction, Mammography, Genetic algorithms, Tumors, Image enhancement, Image processing, Cancer

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Neoadjuvant chemotherapy (NACT) is being used increasingly in the management of patients with breast cancer for systemically reducing the size of primary tumor before surgery in order to improve survival. The clinical response of patients to NACT is correlated with reduced or abolished of their primary tumor, which is important for treatment in the next stage. Recently, the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used for evaluation of the response of patients to NACT. To measure this correlation, we extracted the dynamic features from the DCE- MRI and performed association analysis between these features and the clinical response to NACT. In this study, 59 patients are screened before NATC, of which 47 are complete or partial response, and 12 are no response. We segmented the breast areas depicted on each MR image by a computer-aided diagnosis (CAD) scheme, registered images acquired from the sequential MR image scan series, and calculated eighteen features extracted from DCE-MRI. We performed SVM with the 18 features for classification between patients of response and no response. Furthermore, 6 of the 18 features are selected to refine the classification by using Genetic Algorithm. The accuracy, sensitivity and specificity are 87%, 95.74% and 50%, respectively. The calculated area under a receiver operating characteristic (ROC) curve is 0.79±0.04. This study indicates that the features of DCE-MRI of breast cancer are associated with the response of NACT. Therefore, our method could be helpful for evaluation of NACT in treatment of breast cancer.

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