Evaluating Cancer-Related Biomarkers Based on Pathological Images: A Systematic Review

doi:10.3389/fonc.2021.763527

	Evaluating Cancer-Related Biomarkers Based on Pathological Images: A Systematic Review
	Xie, Xiaoliang 2,3; Wang, Xulin 1; Liang, Yuebin 9,10; Yang, Jingya 8,9,10; Wu, Yan 9,10; Li, Li 6; Sun, Xin 7; Bing, Pingping 4; He, Binsheng 4; Tian, Geng 5,9,10
刊名	FRONTIERS IN ONCOLOGY
	2021-11-10
卷号	11
关键词	histopathological image analysis cancer biomarker deep learning color normalization feature extraction
ISSN号	2234-943X
DOI	10.3389/fonc.2021.763527
通讯作者	Tian, Geng(tiang@geneis.cn) ; Shi, Xiaoli(shixl@geneis.cn)
英文摘要	Many diseases are accompanied by changes in certain biochemical indicators called biomarkers in cells or tissues. A variety of biomarkers, including proteins, nucleic acids, antibodies, and peptides, have been identified. Tumor biomarkers have been widely used in cancer risk assessment, early screening, diagnosis, prognosis, treatment, and progression monitoring. For example, the number of circulating tumor cell (CTC) is a prognostic indicator of breast cancer overall survival, and tumor mutation burden (TMB) can be used to predict the efficacy of immune checkpoint inhibitors. Currently, clinical methods such as polymerase chain reaction (PCR) and next generation sequencing (NGS) are mainly adopted to evaluate these biomarkers, which are time-consuming and expansive. Pathological image analysis is an essential tool in medical research, disease diagnosis and treatment, functioning by extracting important physiological and pathological information or knowledge from medical images. Recently, deep learning-based analysis on pathological images and morphology to predict tumor biomarkers has attracted great attention from both medical image and machine learning communities, as this combination not only reduces the burden on pathologists but also saves high costs and time. Therefore, it is necessary to summarize the current process of processing pathological images and key steps and methods used in each process, including: (1) pre-processing of pathological images, (2) image segmentation, (3) feature extraction, and (4) feature model construction. This will help people choose better and more appropriate medical image processing methods when predicting tumor biomarkers.
资助项目	Natural Science Foundation of Hunan, China[2018JJ3570] ; Major Project for New Generation of AI[2018AAA0100400] ; National Natural Science Foundation of Hunan[2018JJ2098] ; National Natural Science Foundation of China[11571052] ; National Natural Science Foundation of China[11731012]
WOS关键词	BREAST-CANCER ; ACTIVE CONTOUR ; NUCLEI SEGMENTATION ; SURVIVAL ; GRADE ; NORMALIZATION ; PROSTATE ; MODEL
WOS研究方向	Oncology
语种	英语
出版者	FRONTIERS MEDIA SA
WOS记录号	WOS:000733726200001
资助机构	Natural Science Foundation of Hunan, China ; Major Project for New Generation of AI ; National Natural Science Foundation of Hunan ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/126968]
专题	中国科学院合肥物质科学研究院
通讯作者	Tian, Geng; Shi, Xiaoli
作者单位	1.Cent Hosp Jia Mu Si City, Dept Surg Oncol, Jia Mu Si, Peoples R China 2.Ningxia Med Univ, Coll Clin Med, Yinchuan, Ningxia, Peoples R China 3.Ningxia Med Univ, Gen Hosp, Dept Colorectal Surg, Yinchuan, Ningxia, Peoples R China 4.Changsha Med Univ, Acad Workstat, Changsha, Peoples R China 5.Univ Chinese Acad Sci, Zhejiang Canc Hosp, Canc Hosp,IBMC, Chinese Acad Sci,Inst Basic Med & Canc IBMC,BGI C, Hangzhou, Peoples R China 6.Beijing Shanghe Jiye Biotech Co Ltd, Beijing, Peoples R China 7.Cent Hosp Jia Mu Si City, Dept Med Affairs, Jia Mu Si, Peoples R China 8.Anhui Univ Technol, Sch Elect & Informat Engn, Maanshan, Peoples R China 9.Qingdao Geneis Inst Big Data Min & Precis Med, Qingdao, Peoples R China 10.Geneis Beijing Co Ltd, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Xie, Xiaoliang,Wang, Xulin,Liang, Yuebin,et al. Evaluating Cancer-Related Biomarkers Based on Pathological Images: A Systematic Review[J]. FRONTIERS IN ONCOLOGY,2021,11.
APA	Xie, Xiaoliang.,Wang, Xulin.,Liang, Yuebin.,Yang, Jingya.,Wu, Yan.,...&Shi, Xiaoli.(2021).Evaluating Cancer-Related Biomarkers Based on Pathological Images: A Systematic Review.FRONTIERS IN ONCOLOGY,11.
MLA	Xie, Xiaoliang,et al."Evaluating Cancer-Related Biomarkers Based on Pathological Images: A Systematic Review".FRONTIERS IN ONCOLOGY 11(2021).