AUTHOR=Liu Yiqing , Li Xi , Zheng Aiping , Zhu Xihan , Liu Shuting , Hu Mengying , Luo Qianjiang , Liao Huina , Liu Mubiao , He Yonghong , Chen Yupeng 

TITLE=Predict Ki-67 Positive Cells in H&E-Stained Images Using Deep Learning Independently From IHC-Stained Images

JOURNAL=Frontiers in Molecular Biosciences

VOLUME=Volume 7 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2020.00183

DOI=10.3389/fmolb.2020.00183

ISSN=2296-889X

ABSTRACT=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.