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Can Machine-Learning-based Radiomics of Whole Tumor on MR Multiparametric Maps Predict the Ki-67 index of Breast Cancer?

Author:Tianwen Xie  Qiufeng Zhao  Caixia Fu  Robert Grimm  Yajia Gu  Weijun Peng  

Institution:Fudan University Shanghai Cancer Center  Longhua Hospital, Shanghai University of Traditional Chinese Medicine  Siemens Healthcare  Siemens Healthcare  Siemens Shenzhen Magnetic Resonance, Shenzhen, China  

Session Type:Oral  

Session Date:Monday, 13 May 2019  

Session Time:16:00  

Session Name:Breast  

Program Number:0285  

Presentation Time:17:36   

Room Number:Room 513D-F  

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Radiomics Signature on preoperative MR imaging:To predict pathological complete response and disease-free survival in patientes with Triple-negative breast cancer(TNBC) to neoadjuvant chemotherapy (NAC)

Author:Zhe Wang  He Wang  Bing Qing Xia  Yajia Gu  

Institution:Centre for Computational Systems Fudan University  Fudan University Cancer Hospital  Shanghai Center for Mathematical Sciences  

Session Type:Power Pitch  

Session Date:Tuesday, 14 May 2019  

Session Time:15:45  

Session Name:Pitch: Breast Power Pitch  

Program Number:0597  

Presentation Time:15:45   

Room Number:Power Pitch Theater C - Exhibition Hall  

Computer Number:

Radiomics Signature on preoperative MR imaging:To predict pathological complete response and disease-free survival in patientes with Triple-negative breast cancer(TNBC) to neoadjuvant chemotherapy (NAC)

Author:Zhe Wang  He Wang  Bing Qing Xia  Yajia Gu  

Institution:Centre for Computational Systems Fudan University  Fudan University Cancer Hospital  Shanghai Center for Mathematical Sciences  

Session Type:Power Pitch Poster  

Session Date:Tuesday, 14 May 2019  

Session Time:16:45  

Session Name:Poster: Breast Power Poster  

Program Number:0597  

Presentation Time:16:45   

Room Number:Power Pitch Theater C - Exhibition Hall  

Computer Number:Plasma 38  

Whole-Tumor Histogram and Textural Analysis of Model-based T2 Mapping for the Ki-67 Labeling Index of Breast Cancer