A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology.
A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology.
复制标题
从组织病理学角度预测 EBV 相关胃癌的深度学习模型和人机融合
DOI：
10.1038/s41467-022-30459-5
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发表时间：
2022-05-19
影响因子：
16.6
通讯作者：
中科院分区：
文献类型：
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作者：
Epstein–Barr virus-associated gastric cancer (EBVaGC) shows a robust response to immune checkpoint inhibitors. Therefore, a cost-efficient and accessible tool is needed for discriminating EBV status in patients with gastric cancer. Here we introduce a deep convolutional neural network called EBVNet and its fusion with pathologists for predicting EBVaGC from histopathology. The EBVNet yields an averaged area under the receiver operating curve (AUROC) of 0.969 from the internal cross validation, an AUROC of 0.941 on an external dataset from multiple institutes and an AUROC of 0.895 on The Cancer Genome Atlas dataset. The human-machine fusion significantly improves the diagnostic performance of both the EBVNet and the pathologist. This finding suggests that our EBVNet could provide an innovative approach for the identification of EBVaGC and may help effectively select patients with gastric cancer for immunotherapy. Epstein–Barr virus-associated gastric cancer shows a robust response to immune checkpoint inhibitors. Here the authors introduce a deep convolutional neural network and its fusion with pathologists for predicting it from histopathology.
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影响因子：
78.8
作者：
Bera, Kaustav;Schalper, Kurt A.;Madabhushi, Anant
通讯作者：
Madabhushi, Anant
影响因子：
3.9
作者：
Xie, Tong;Liu, Yiqiang;Peng, Zhi
通讯作者：
Peng, Zhi
DOI：
10.1016/s2589-7500(21)00133-3
发表时间：
2021-10
期刊：
The Lancet. Digital health
影响因子：
--
作者：
Muti HS;Heij LR;Keller G;Kohlruss M;Langer R;Dislich B;Cheong JH;Kim YW;Kim H;Kook MC;Cunningham D;Allum WH;Langley RE;Nankivell MG;Quirke P;Hayden JD;West NP;Irvine AJ;Yoshikawa T;Oshima T;Huss R;Grosser B;Roviello F;d'Ignazio A;Quaas A;Alakus H;Tan X;Pearson AT;Luedde T;Ebert MP;Jäger D;Trautwein C;Gaisa NT;Grabsch HI;Kather JN
通讯作者：
Kather JN
DOI：
10.1109/tpami.2022.3152247
发表时间：
2023-01-01
影响因子：
23.6
作者：
Han, Kai;Wang, Yunhe;Tao, Dacheng
通讯作者：
Tao, Dacheng
影响因子：
51.1
作者：
Yamashita, Rikiya;Long, Jin;Shen, Jeanne
通讯作者：
Shen, Jeanne