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Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images

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WOS体系：
Pubmed体系：

收录情况： ◇ SCIE ◇ 自然指数

作者：
Wang Weiwei[1] * ; Zhao Yuanshen[2,3] * ; Teng Lianghong[4] * ; Yan Jing[5] * ; Guo Yang[6] ; Qiu Yuning[7] ; Ji Yuchen[7] ; Yu Bin[7] ; Pei Dongling[7] ; Duan Wenchao[7] ; Wang Minkai[7] ; Wang Li[1] ; Duan Jingxian[2,3] ; Sun Qiuchang[2,3] ; Wang Shengnan[4] ; Duan Huanli[4] ; Sun Chen[7] ; Guo Yu[7] ; Luo Lin[7] ; Guo Zhixuan[7] ; Guan Fangzhan[7] ; Wang Zilong[7] ; Xing Aoqi[7] ; Liu Zhongyi[7] ; Zhang Hongyan[1] ; Cui Li[1] ; Zhang Lan[1] ; Jiang Guozhong[1] ; Yan Dongming[7] ; Liu Xianzhi[7] ; Zheng Hairong[2,3,8,9] ; Liang Dong[2,3,8,10] ; Li Wencai[1] ; Li Zhi-Cheng[2,3,8,10] ; Zhang Zhenyu[7] ;
机构： [1]Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China. [2]Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. [3]University of Chinese Academy of Sciences, Beijing, China. [4]Department of Pathology, Xuanwu Hospital, Capital Medical University, Beijing, China. [5]Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China. [6]Department of Neurosurgery, Henan Provincial People’s Hospital, Zhengzhou, Henan, China. [7]Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China. [8]The Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, Shenzhen, China. [9]National Innovation Center for Advanced Medical Devices, Shenzhen, China. [10]National InnovationCenter for AdvancedMedical Devices, Shenzhen,China.
出处：
DOI： 10.1038/s41467-023-41195-9
ISSN：

摘要：
Current diagnosis of glioma types requires combining both histological features and molecular characteristics, which is an expensive and time-consuming procedure. Determining the tumor types directly from whole-slide images (WSIs) is of great value for glioma diagnosis. This study presents an integrated diagnosis model for automatic classification of diffuse gliomas from annotation-free standard WSIs. Our model is developed on a training cohort (n = 1362) and a validation cohort (n = 340), and tested on an internal testing cohort (n = 289) and two external cohorts (n = 305 and 328, respectively). The model can learn imaging features containing both pathological morphology and underlying biological clues to achieve the integrated diagnosis. Our model achieves high performance with area under receiver operator curve all above 0.90 in classifying major tumor types, in identifying tumor grades within type, and especially in distinguishing tumor genotypes with shared histological features. This integrated diagnosis model has the potential to be used in clinical scenarios for automated and unbiased classification of adult-type diffuse gliomas.© 2023. Springer Nature Limited.

基金：
Key-Area Research and Development Program of Guangdong Province 2021B0101420006 (Z.-C.L.), the National Natural Science Foundation of China U20A2017 (Z.-C. L.), 82273493 (Z.-Y.Z.), and U1904148 (W.-W.W.), the Natural Science Foundation of Henan Province for Excellent Young Scholars No.232300421057 (Z.-Y.Z.), Henan Province Outstanding Young Talent Project in Health Science and Technology Innovation for Young and Middle-aged People YXKC2022035 (W.-W.W.), Henan Province Key Research and Development (R & D) program 232300421125 (W.-W.W.), the Science and Technology Program of Henan Province No.202102310136 (Y.-C.J.), 212102310113 (B.Y.), Guangdong Basic and Applied Basic Research Foundation 2020B1515120046 (Z.-C.L.), Guangdong Key Project 2018B030335001 (Z.-C.L.), Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province 2020B1212060051 (Z.-C.L.), and Guangzhou Key Research and Development Program 202007030002 (Z.-C.L.).
语种：
外文
被引次数：
WOS：
PubmedID：
中科院(CAS)分区：
出版当年[2022]版：
大类 | 1 区 综合性期刊
小类 | 1 区 综合性期刊
最新[2023]版：
大类 | 1 区 综合性期刊
小类 | 1 区 综合性期刊
JCR分区：
出版当年[2021]版：
Q1 MULTIDISCIPLINARY SCIENCES
最新[2023]版：
Q1 MULTIDISCIPLINARY SCIENCES

影响因子： 最新[2023版] 最新五年平均 出版当年[2021版] 出版当年五年平均 出版前一年[2020版] 出版后一年[2022版]

第一作者：
第一作者机构： [1]Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
共同第一作者：
通讯作者：
通讯机构： [2]Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. [3]University of Chinese Academy of Sciences, Beijing, China. [8]The Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, Shenzhen, China. [10]National InnovationCenter for AdvancedMedical Devices, Shenzhen,China.
推荐引用方式(GB/T 7714)：
Wang Weiwei,Zhao Yuanshen,Teng Lianghong,et al.Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images[J].NATURE COMMUNICATIONS.2023,14(1):doi:10.1038/s41467-023-41195-9.
APA：
Wang Weiwei,Zhao Yuanshen,Teng Lianghong,Yan Jing,Guo Yang...&Zhang Zhenyu.(2023).Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images.NATURE COMMUNICATIONS,14,(1)
MLA：
Wang Weiwei,et al."Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images".NATURE COMMUNICATIONS 14..1(2023)

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