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

Frontiers in Molecular Biosciences

Xi Li¹  Qianjiang Luo¹  Huina Liao¹  Mengying Hu²  Yiqing Liu²  Yonghong He²  Shuting Liu²  Mubiao Liu³  Aiping Zheng⁴  Yupeng Chen⁵  Xihan Zhu⁶ 

[1] Department of Gastroenterology, Peking University Shenzhen Hospital, Shenzhen, China;Department of Life and Health, Tsinghua Shenzhen International Graduate School, Shenzhen, China;Department of Obstetrics and Gynecology, Guangdong Provincial People’s Hospital, Guangzhou, China;Department of Pathology, Peking University Shenzhen Hospital, Shenzhen, China;Peng Cheng Laboratory, Shenzhen, China;School of Traditional Chinese Medicine, Capital Medical University, Beijing, China;

关键词: digital pathology;    immunohistochemistry;    Ki-67;    deep learning;    fully convolutional network;    neuroendocrine tumor;   

DOI  :  10.3389/fmolb.2020.00183

来源: DOAJ

【 摘 要 】

ObjectiveTo 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.MethodsIn 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.ResultsThe 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 SignificanceOur 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 ImplementationThe main program is available at https://github.com/liuyiqing2018/predict_Ki-67_from_HE

【 授权许可】

Unknown