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Paper Abstract and Keywords
Presentation 2021-12-13 13:30
Fusion model using MRI-based radiomics with deep learning feature for glioma IDH mutation predicting
Shi Xiaoyu, Zhang Xinran, Yiwamoto Yutaro (Ritsu Univ.), Jingliang Cheng, Jie Bai, Guohua Zhao (Department of Magnetic Resonance Imaging, The First Affiliated H), Chen yenwei (Ritsu Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) According to the 2016 World Health Organization classification scheme for gliomas, Isocitrate
dehydrogenase(IDH) status is a very important basis for diagnosis. It is believed that there is a strong relationship between
IDH status and glioma prognosis. Therefore, it is necessary to predict IDH status for the treatment of glioma. In this paper, we
proposed a fusion model using MRI-based radiomics with deep learning feature for glioma IDH mutation prediction. The
effectiveness of the proposed method is validated on our private from First Affiliated Hospital of Zhengzhou
University(FHZU), China. The F1score of the proposed method is 0.84, which is superior to state-of-the-art methods.
Keyword (in Japanese) (See Japanese page) 
(in English) Isocitrate dehydrogenase / Glioma / Radiomics / Deep learning / / / /  
Reference Info. ITE Tech. Rep., vol. 45, no. 39, ME2021-93, pp. 21-24, Dec. 2021.
Paper # ME2021-93 
Date of Issue 2021-12-06 (ME) 
ISSN Print edition: ISSN 1342-6893  Online edition: ISSN 2424-1970
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Conference Information
Committee ME  
Conference Date 2021-12-13 - 2021-12-13 
Place (in Japanese) (See Japanese page) 
Place (in English) online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To ME 
Conference Code 2021-12-ME 
Language English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Fusion model using MRI-based radiomics with deep learning feature for glioma IDH mutation predicting 
Sub Title (in English)  
Keyword(1) Isocitrate dehydrogenase  
Keyword(2) Glioma  
Keyword(3) Radiomics  
Keyword(4) Deep learning  
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1st Author's Name Shi Xiaoyu  
1st Author's Affiliation Ritsumeikan University (Ritsu Univ.)
2nd Author's Name Zhang Xinran  
2nd Author's Affiliation Ritsumeikan University (Ritsu Univ.)
3rd Author's Name Yiwamoto Yutaro  
3rd Author's Affiliation Ritsumeikan University (Ritsu Univ.)
4th Author's Name Jingliang Cheng  
4th Author's Affiliation Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University (Department of Magnetic Resonance Imaging, The First Affiliated H)
5th Author's Name Jie Bai  
5th Author's Affiliation Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University (Department of Magnetic Resonance Imaging, The First Affiliated H)
6th Author's Name Guohua Zhao  
6th Author's Affiliation Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University (Department of Magnetic Resonance Imaging, The First Affiliated H)
7th Author's Name Chen yenwei  
7th Author's Affiliation Ritsumeikan University (Ritsu Univ.)
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Speaker
Date Time 2021-12-13 13:30:00 
Presentation Time 30 
Registration for ME 
Paper # ITE-ME2021-93 
Volume (vol) ITE-45 
Number (no) no.39 
Page pp.21-24 
#Pages ITE-4 
Date of Issue ITE-ME-2021-12-06 


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