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Abstract #0187

A voxel-wise composition ratio of DCE-MRI time-intensity curve profiles allows for visualizing and quantifying hemodynamic heterogeneity

Bingyu Yao1,2, Zhou Liu3, Yumin Chen2, Jie Wen3, Meng Wang3, Ya Ren3, Dong Liang1, Xin Liu1, Hairong Zheng1, Dehong Luo3, and Na Zhang1
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 2College of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China, 3Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China

Synopsis

Keywords: Breast, CancerWe propose a novel model-free and data-driven approach, i.e., voxel-wise composition ratio on 19 dynamic contrast-enhanced MRI (DCE-MRI) time-intensity curve (TIC) profiles (Type-19) to visualize and quantify spatial hemodynamic heterogeneity. The proposed quantitative method for breast tumor was evaluated and compared with the two existing methods (qualitative and semi-quantitative methods) in 4 different clinical applications. In distinguishing malignancy on breast cancer lesions and predicting tumor proliferation status, we found that the machine learning model based on the Type-19 feature outperformed other two models in the validation set.

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