BrcaSeg:A Deep Learning Approach for Tissue Quantification and Genomic Correlations of Histopathological Images  

作　　者：Zixiao Lu Xiaohui Zhan Yi Wu Jun Cheng Wei Shao Dong Ni Zhi Han Jie Zhang Qianjin Feng Kun Huang 

机构地区：[1]Guangdong Provincial Key Laboratory of Medical Image Processing,School of Biomedical Engineering,Southern Medical University,Guangzhou 510515,China [2]National-Regional Key Technology Engineering Laboratory for Medical Ultrasound,School of Biomedical Engineering,Health Science Center,Shenzhen University,Shenzhen 518060,China [3]Department of Medicine,Indiana University School of Medicine,Indianapolis,IN 46202,USA [4]Department of Medical and Molecular Genetics,Indiana University School of Medicine,Indianapolis,IN 46202,USA [5]Department of Biostatistics and Health Data Science,Indiana University School of Medicine,Indianapolis,IN 46202,USA [6]Regenstrief Institute,Indianapolis,IN 46202,USA

出　　处：《Genomics, Proteomics & Bioinformatics》2021年第6期1032-1042,共11页基因组蛋白质组与生物信息学报（英文版）

基　　金：supported by Indiana University Precision Health Initiative to KH and JZ;the NSFC-Guangdong Joint Fund of China (Grant No. U1501256) to QF;Shenzhen Peacock Plan (Grant No. KQTD2016053112051497) to XZ and ND.

摘　　要：Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and progression.Distinguishing stroma from epithelial tissues is critically important for spatial characterization of the tumor microenvironment.Here,we propose BrcaSeg,an image analysis pipeline based on a convolutional neural network(CNN)model to classify epithelial and stromal regions in whole-slide hematoxylin and eosin(H&E)stained histopathological images.The CNN model is trained using well-annotated breast cancer tissue microarrays and validated with images from The Cancer Genome Atlas(TCGA)Program.BrcaSeg achieves a classification accuracy of 91.02%,which outperforms other state-of-the-art methods.Using this model,we generate pixel-level epithelial/stromal tissue maps for 1000 TCGA breast cancer slide images that are paired with gene expression data.We subsequently estimate the epithelial and stromal ratios and perform correlation analysis to model the relationship between gene expression and tissue ratios.Gene Ontology(GO)enrichment analyses of genes that are highly correlated with tissue ratios suggest that the same tissue is associated with similar biological processes in different breast cancer subtypes,whereas each subtype also has its own idiosyncratic biological processes governing the development of these tissues.Taken all together,our approach can lead to new insights in exploring relationships between image-based phenotypes and their underlying genomic events and biological processes for all types of solid tumors.BrcaSeg can be accessed at https://github.com/Serian1992/ImgBio.

关 键 词：Whole-slide tissue image Computational pathology Deep learning Integrative genomics Breast cancer 

分 类 号：R318[医药卫生—生物医学工程] TP391.41[医药卫生—基础医学] TP18[自动化与计算机技术—计算机应用技术]