人工智能与数字病理学在结直肠癌免疫肿瘤学中的作用。
Role of AI and digital pathology for colorectal immuno-oncology.
作者信息
Bilal Mohsin, Nimir Mohammed, Snead David, Taylor Graham S, Rajpoot Nasir
机构信息
Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK.
Department of Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK.
出版信息
Br J Cancer. 2023 Jan;128(1):3-11. doi: 10.1038/s41416-022-01986-1. Epub 2022 Oct 1.
Immunotherapy deals with therapeutic interventions to arrest the progression of tumours using the immune system. These include checkpoint inhibitors, T-cell manipulation, cytokines, oncolytic viruses and tumour vaccines. In this paper, we present a survey of the latest developments on immunotherapy in colorectal cancer (CRC) and the role of artificial intelligence (AI) in this context. Among these, microsatellite instability (MSI) is perhaps the most popular IO biomarker globally. We first discuss the MSI status of tumours, its implications for patient management, and its relationship to immune response. In recent years, several aspiring studies have used AI to predict the MSI status of patients from digital whole-slide images (WSIs) of routine diagnostic slides. We present a survey of AI literature on the prediction of MSI and tumour mutation burden from digitised WSIs of haematoxylin and eosin-stained diagnostic slides. We discuss AI approaches in detail and elaborate their contributions, limitations and key takeaways to drive future research. We further expand this survey to other IO-related biomarkers like immune cell infiltrates and alternate data modalities like immunohistochemistry and gene expression. Finally, we underline possible future directions in immunotherapy for CRC and promise of AI to accelerate this exploration for patient benefits.
免疫疗法涉及利用免疫系统进行治疗干预以阻止肿瘤进展。这些包括检查点抑制剂、T细胞操控、细胞因子、溶瘤病毒和肿瘤疫苗。在本文中,我们概述了结直肠癌(CRC)免疫疗法的最新进展以及人工智能(AI)在此背景下的作用。其中,微卫星不稳定性(MSI)可能是全球最受欢迎的免疫肿瘤学生物标志物。我们首先讨论肿瘤的MSI状态、其对患者管理的影响以及它与免疫反应的关系。近年来,一些有前景的研究利用人工智能从常规诊断切片的数字全切片图像(WSIs)预测患者的MSI状态。我们概述了关于从苏木精和伊红染色的诊断切片的数字化WSIs预测MSI和肿瘤突变负担的人工智能文献。我们详细讨论人工智能方法,并阐述它们的贡献、局限性和关键要点,以推动未来研究。我们进一步将此概述扩展到其他与免疫肿瘤学相关的生物标志物,如免疫细胞浸润,以及其他数据模式,如免疫组织化学和基因表达。最后,我们强调CRC免疫疗法未来可能的方向以及人工智能有望加速这一探索以造福患者。