机构：[1]Department of Life and Health, Tsinghua Shenzhen International Graduate School, Shenzhen, China,深圳市康宁医院深圳医学信息中心[2]Department of Gastroenterology, Peking University Shenzhen Hospital, Shenzhen, China,北京大学深圳医院深圳市康宁医院深圳医学信息中心[3]Department of Pathology, Peking University Shenzhen Hospital, Shenzhen, China,北京大学深圳医院深圳市康宁医院深圳医学信息中心[4]School of Traditional Chinese Medicine, Capital Medical University, Beijing, China,[5]Department of Obstetrics and Gynecology, Guangdong Provincial People’s Hospital, Guangzhou, China,[6]Peng Cheng Laboratory, Shenzhen, China
Objective: To obtain molecular information in slides directly from H&E staining slides, which apparently display morphological information, to show that some differences in molecular level have already encoded in morphology. Methods: In this paper, we selected Ki-67-expression as the representative of molecular information. We proposed a method that can predict Ki-67 positive cells directly from H&E stained slides by a deep convolutional network model. To train this model, we constructed a dataset containing Ki-67 negative or positive cell images and background images. These images were all extracted from H&E stained WSIs and the Ki-67 expression was acquired from the corresponding IHC stained WSIs. The trained model was evaluated both on classification performance and the ability to quantify Ki-67 expression in H&E stained images. Results: The model achieved an average accuracy of 0.9371 in discrimination of Ki-67 negative cell images, positive cell images and background images. As for evaluation of quantification performance, the correlation coefficient between the quantification results of H&E stained images predicted by our model and that of IHC stained images obtained by color channel filtering is 0.80. Conclusion and Significance: Our study indicates that the deep learning model has a good performance both on prediction of Ki-67 positive cells and quantification of Ki-67 expression in cancer samples stained by H&E. More generally, this study shows that deep learning is a powerful tool in exploring the relationship between morphological information and molecular information. Availability and Implementation: The main program is available at https://github.com/liuyiqing2018/predict_Ki- 67_from_HE
基金：
National Science Foundation of China (NSFC)National Natural Science Foundation of China (NSFC) [61875102, 81871395, 61675113]; Science and Technology Research Program of Shenzhen City [JCYJ20170816161836562, JCYJ20170817111912585, JCYJ20160427183803458, JCYJ201704 12171856582, JCYJ20180508152528735]; Oversea cooperation foundation; Graduate School at Shenzhen, Tsinghua University [HW2018007]
第一作者机构：[1]Department of Life and Health, Tsinghua Shenzhen International Graduate School, Shenzhen, China,
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通讯作者：
推荐引用方式(GB/T 7714)：
Liu Yiqing,Li Xi,Zheng Aiping,et al.Predict Ki-67 Positive Cells in H&E-Stained Images Using Deep Learning Independently From IHC-Stained Images[J].FRONTIERS IN MOLECULAR BIOSCIENCES.2020,7:doi:10.3389/fmolb.2020.00183.
APA：
Liu, Yiqing,Li, Xi,Zheng, Aiping,Zhu, Xihan,Liu, Shuting...&Chen, Yupeng.(2020).Predict Ki-67 Positive Cells in H&E-Stained Images Using Deep Learning Independently From IHC-Stained Images.FRONTIERS IN MOLECULAR BIOSCIENCES,7,
MLA：
Liu, Yiqing,et al."Predict Ki-67 Positive Cells in H&E-Stained Images Using Deep Learning Independently From IHC-Stained Images".FRONTIERS IN MOLECULAR BIOSCIENCES 7.(2020)
