Evaluating Cancer-Related Biomarkers Based on Pathological Images: A Systematic Review
文献类型：期刊论文
| 作者 | Xie, Xiaoliang9,10; Wang, Xulin8; Liang, Yuebin6,7; Yang, Jingya5,6,7; Wu, Yan6,7; Li, Li4; Sun, Xin3; Bing, Pingping2; He, Binsheng2; Tian, Geng1,6,7 |
| 刊名 | FRONTIERS IN ONCOLOGY
 |
| 出版日期 | 2021-11-10 |
| 卷号 | 11 |
| ISSN号 | 2234-943X |
| 关键词 | histopathological image analysis cancer biomarker deep learning color normalization feature extraction |
| 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. |
| WOS关键词 | BREAST-CANCER ; ACTIVE CONTOUR ; NUCLEI SEGMENTATION ; SURVIVAL ; GRADE ; NORMALIZATION ; PROSTATE ; MODEL |
| 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 |
| 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.Univ Chinese Acad Sci, Zhejiang Canc Hosp, Canc Hosp,IBMC, Chinese Acad Sci,Inst Basic Med & Canc IBMC,BGI C, Hangzhou, Peoples R China 2.Changsha Med Univ, Acad Workstat, Changsha, Peoples R China 3.Cent Hosp Jia Mu Si City, Dept Med Affairs, Jia Mu Si, Peoples R China 4.Beijing Shanghe Jiye Biotech Co Ltd, Beijing, Peoples R China 5.Anhui Univ Technol, Sch Elect & Informat Engn, Maanshan, Peoples R China 6.Qingdao Geneis Inst Big Data Min & Precis Med, Qingdao, Peoples R China 7.Geneis Beijing Co Ltd, Beijing, Peoples R China 8.Cent Hosp Jia Mu Si City, Dept Surg Oncol, Jia Mu Si, Peoples R China 9.Ningxia Med Univ, Coll Clin Med, Yinchuan, Ningxia, Peoples R China 10.Ningxia Med Univ, Gen Hosp, Dept Colorectal Surg, Yinchuan, Ningxia, 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). |
入库方式： OAI收割
来源：合肥物质科学研究院
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