Breast Cancer Molecular Subtype Prediction on Pathological Images with Discriminative Patch Selection and Multi-Instance Learning

doi:10.3389/fonc.2022.858453

	Breast Cancer Molecular Subtype Prediction on Pathological Images with Discriminative Patch Selection and Multi-Instance Learning
	Liu, Hong 1; Xu, Wen-Dong 1,2; Shang, Zi-Hao 1,2; Wang, Xiang-Dong 1; Zhou, Hai-Yan 3; Ma, Ke-Wen 3; Zhou, Huan 3; Qi, Jia-Lin 3; Jiang, Jia-Rui 3; Tan, Li-Lan 3; Zeng, Hui-Min 3; Cai, Hui-Juan 3; Wang, Kuan-Song 3,4; Qian, Yue-Liang 1
	2022-04-14
发表期刊	FRONTIERS IN ONCOLOGY
ISSN	2234-943X
卷号	12 页码:11
摘要	Molecular subtypes of breast cancer are important references to personalized clinical treatment. For cost and labor savings, only one of the patient's paraffin blocks is usually selected for subsequent immunohistochemistry (IHC) to obtain molecular subtypes. Inevitable block sampling error is risky due to the tumor heterogeneity and could result in a delay in treatment. Molecular subtype prediction from conventional H&E pathological whole slide images (WSI) using the AI method is useful and critical to assist pathologists to pre-screen proper paraffin block for IHC. It is a challenging task since only WSI-level labels of molecular subtypes from IHC can be obtained without detailed local region information. Gigapixel WSIs are divided into a huge amount of patches to be computationally feasible for deep learning, while with coarse slide-level labels, patch-based methods may suffer from abundant noise patches, such as folds, overstained regions, or non-tumor tissues. A weakly supervised learning framework based on discriminative patch selection and multi-instance learning was proposed for breast cancer molecular subtype prediction from H&E WSIs. Firstly, co-teaching strategy using two networks was adopted to learn molecular subtype representations and filter out some noise patches. Then, a balanced sampling strategy was used to handle the imbalance in subtypes in the dataset. In addition, a noise patch filtering algorithm that used local outlier factor based on cluster centers was proposed to further select discriminative patches. Finally, a loss function integrating local patch with global slide constraint information was used to fine-tune MIL framework on obtained discriminative patches and further improve the prediction performance of molecular subtyping. The experimental results confirmed the effectiveness of the proposed AI method and our models outperformed even senior pathologists, which has the potential to assist pathologists to pre-screen paraffin blocks for IHC in clinic.
关键词	pathological image weakly supervised learning molecular subtype breast cancer H&E
DOI	10.3389/fonc.2022.858453
收录类别	SCI
语种	英语
资助项目	Beijing Natural Science Foundation[Z190020] ; National Natural Science Foundation of China[81972490] ; Natural Science Foundation of Hunan Province[2019JJ50781]
WOS研究方向	Oncology
WOS类目	Oncology
WOS记录号	WOS:000795567500001
出版者	FRONTIERS MEDIA SA
引用统计	被引频次：11[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/19541
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Liu, Hong; Wang, Kuan-Song
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Cent South Univ, Xiangya Hosp, Dept Pathol, Changsha, Peoples R China 4.Cent South Univ, Sch Basic Med Sci, Changsha, Peoples R China
推荐引用方式 GB/T 7714	Liu, Hong,Xu, Wen-Dong,Shang, Zi-Hao,et al. Breast Cancer Molecular Subtype Prediction on Pathological Images with Discriminative Patch Selection and Multi-Instance Learning[J]. FRONTIERS IN ONCOLOGY,2022,12:11.
APA	Liu, Hong.,Xu, Wen-Dong.,Shang, Zi-Hao.,Wang, Xiang-Dong.,Zhou, Hai-Yan.,...&Qian, Yue-Liang.(2022).Breast Cancer Molecular Subtype Prediction on Pathological Images with Discriminative Patch Selection and Multi-Instance Learning.FRONTIERS IN ONCOLOGY,12,11.
MLA	Liu, Hong,et al."Breast Cancer Molecular Subtype Prediction on Pathological Images with Discriminative Patch Selection and Multi-Instance Learning".FRONTIERS IN ONCOLOGY 12(2022):11.