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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