肿瘤浸润淋巴细胞
数字化病理学
肿瘤微环境
免疫疗法
数字图像分析
医学
免疫系统
肺癌
癌症研究
计算机科学
病理
免疫学
计算机视觉
作者
Jianghua Wu,Dongmei Lin
出处
期刊:Advances in Anatomic Pathology
[Ovid Technologies (Wolters Kluwer)]
日期:2021-09-21
卷期号:28 (6): 439-445
被引量:9
标识
DOI:10.1097/pap.0000000000000322
摘要
Advances in immunotherapy have increased the need for stratified predictive biomarkers in patients with non-small cell lung cancer. However, precise evaluation of tumor tissue-based immune biomarkers, such as programmed cell death-ligand 1 (PD-L1) and the characteristics of tumor infiltrating lymphocytes (TILs), is a challenge in clinical practice. In recent years, the digitization of whole-slide images of tissue has accelerated the implementation of artificial intelligence (AI) approaches in tumor pathology and provided an opportunity to use AI tools to improve the interpretation of immune biomarkers. This review describes the current challenges in the assessment of PD-L1 scoring and TILs and demonstrates the role of AI in helping pathologists integrate PD-L1 and biomarkers of the tumor immune microenvironment. Computer-aided PD-L1 scoring is highly consistent with pathologists and reduces the variation among interobservers, providing a promising diagnostic tool in pathology clinics. In addition, applications of image analysis algorithms, in combination with multiplex staining, enable in-depth quantitative and spatial analysis of the broader tumor microenvironment. Upon combining digital pathology and AI, an automatic analysis system of PD-L1 and TILs, which was established using a set of digital staining images and deep learning algorithms, might be an effective way to overcome the challenges in the precise assessment of immune biomarkers.
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