Abstract #0187

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

Bingyu Yao^1,2, 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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