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Bin-Ke Yuan
Shenzhen Institute of Neuroscience
Shenzhen
China
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SCHOLARLY PAPERS
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Scholarly Papers (1)
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1.
Radiomic Features from Multi-Modal MRI Combined with Clinical Parameters Predict Molecular Subgroups in Patients with Medulloblastoma
Number of pages: 35
Posted: 13 Nov 2019
Jing Yan
,
Lei Liu
,
Wei-Wei Wang
,
Kay Ka-Wai Li
,
Ke Li
,
Li Wang
, Bin-Ke Yuan,
Hai-Yang Geng
,
Sheng-Hai Zhang
,
Wen-Chao Duan
,
Yun-Bo Zhan
,
Hai-Biao Zhao
,
Tao Sun
,
Chen Sun
,
Wen-Qing Wang
,
Zhen Liu
,
Xuan-Ke Hong
,
Xiang-Xiang Wang
,
Yu Guo
,
Wen-Cai Li
,
Jing-Liang Cheng
,
Xian-Zhi Liu
,
Ho-Keung Ng
,
Zhi-Cheng Li
and
Zhen-Yu Zhang
Zhengzhou University - First Affiliated Hospital, Chinese Academy of Sciences (CAS) - Institute of Biomedical and Health Engineering, Zhengzhou University - First Affiliated Hospital, The Chinese University of Hong Kong (CUHK) - Department of Anatomical and Cellular Pathology, Zhengzhou University - Department of Neurosurgery, Zhengzhou University - First Affiliated Hospital, Shenzhen Institute of Neuroscience, Shenzhen University, Chinese Academy of Sciences (CAS) - Institute of Biomedical and Health Engineering, Zhengzhou University - Department of Neurosurgery, Zhengzhou University - Department of Neurosurgery, Zhengzhou University - Department of Neurosurgery, Zhengzhou University - Department of Neurosurgery, Zhengzhou University - Department of Neurosurgery, Zhengzhou University - Department of Neurosurgery, Zhengzhou University - Department of Neurosurgery, Zhengzhou University - Department of Neurosurgery, Zhengzhou University - Department of Neurosurgery, Zhengzhou University - Department of Neurosurgery, Zhengzhou University - First Affiliated Hospital, Zhengzhou University - First Affiliated Hospital, Zhengzhou University - Department of Neurosurgery, The Chinese University of Hong Kong (CUHK) - Department of Anatomical and Cellular Pathology, Chinese Academy of Sciences (CAS) - Institute of Biomedical and Health Engineering and Zhengzhou University - Department of Neurosurgery
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Abstract:
medulloblastoma, radiomics, molecular subgroups, machine learning, prediction
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