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Abstract #1115

Combination of pharmacokinetic parameters and texture features of DCE-MRI for predicting preoperative classification of breast cancer

Xia Wu1,2,3, Zhou Liu4, Meng Wang4, Zhe Ren1,2,3, Ya Ren4, Jie Wen4, Qian Yang4, Xin Liu1,2,3, Hairong Zheng1,2,3, and Na Zhang1,2,3
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences Synopsis, ShenZhen, China, 2Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, ShenZhen, China, 3CAS key laboratory of health informatics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, ShenZhen, China, 4Department of Radiology, National Cancer Center/Cancer Hospital and Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, ShenZhen, China

We have achieved preoperative classification of breast cancer by combination of pharmacokinetic parameters and texture features using machine learning. Using the information available in each feature space, an appropriate feature fusion method using information from the two feature spaces can help the classification process and improve diagnosis accuracy. Among them, SVM and KNN have better performance.

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Abstract