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

Abstract

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.

• Authors: Xiaoliang Xie, Xulin Wang, Yuebin Liang, Jingya Yang, Yan Wu, Li Li, Xin Sun, Pingping Bing, Binsheng He, Geng Tian, Xiaoli Shi
• Journal: Frontiers in oncology (Front Oncol) Vol. 11 Pg. 763527 ( 2021) ISSN: 2234-943X [Print] Switzerland
• PMID: 34900711 (Publication Type: Journal Article, Review)
• Copyright: Copyright © 2021 Xie, Wang, Liang, Yang, Wu, Li, Sun, Bing, He, Tian and Shi.