Deep learning features from diffusion tensor imaging improve glioma stratification and identify risk groups with distinct molecular pathway activities

BACKGROUND: To develop and validate a deep learning signature (DLS) from diffusion tensor imaging (DTI) for predicting overall survival in patients with infiltrative gliomas, and to investigate the biological pathways underlying the developed DLS. METHODS: The DLS was developed based on a deep learn...

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Detalles Bibliográficos
Autores principales: Yan, Jing, Zhao, Yuanshen, Chen, Yinsheng, Wang, Weiwei, Duan, Wenchao, Wang, Li, Zhang, Shenghai, Ding, Tianqing, Liu, Lei, Sun, Qiuchang, Pei, Dongling, Zhan, Yunbo, Zhao, Haibiao, Sun, Tao, Sun, Chen, Wang, Wenqing, Liu, Zhen, Hong, Xuanke, Wang, Xiangxiang, Guo, Yu, Li, Wencai, Cheng, Jingliang, Liu, Xianzhi, Lv, Xiaofei, Li, Zhi-Cheng, Zhang, Zhenyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Research Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8479635/
https://www.ncbi.nlm.nih.gov/pubmed/34563923
http://dx.doi.org/10.1016/j.ebiom.2021.103583

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8479635/
https://www.ncbi.nlm.nih.gov/pubmed/34563923
http://dx.doi.org/10.1016/j.ebiom.2021.103583

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