Artificial Intelligence in Digital Pathology to Advance Cancer Immunotherapy.
Artificial Intelligence in Digital Pathology to Advance Cancer Immunotherapy.
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发表时间：
2022
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通讯作者：
Wu J
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作者：
Chen P;Zhang J;Wu J
免疫检查点抑制剂（ICI）已经彻底改变了许多恶性肿瘤的治疗。例如，在肺癌中，只有20~30%的患者可以从ICI单药治疗中获得持久的临床获益。组织学和分子特征，如组织学类型，PD-L1表达和肿瘤突变负荷（TMB），在免疫治疗时代选择适当的癌症治疗方案中发挥着至关重要的作用。不幸的是，现有的特征都不是唯一的预测生物标志物。因此，迫切需要确定更有效的生物标志物，以识别可能从ICI中获得最大益处的患者。在临床流程中采用数字病理学，由人工智能（AI）特别是深度学习提供动力，促进了组织切片的自动化分析。随着多重生物成像技术的突破，研究人员可以全面表征肿瘤微环境，包括不同免疫细胞的分布，功能和相互作用。在这里，我们简要总结了最近在数字病理学方面的人工智能研究，并分享了我们对推动免疫治疗生物标志物发展的新兴范式和方向的看法。
Immune-checkpoint inhibitors (ICIs) have revolutionized the treatment of many malignancies. For instance, in lung cancer, however, only 20~30% of patients can achieve durable clinical benefits from ICI monotherapy. Histopathologic and molecular features such as histological type, PD-L1 expression, and tumor mutation burden (TMB), play a paramount role in selecting appropriate regimens for cancer treatment in the era of immunotherapy. Unfortunately, none of the existing features are exclusive predictive biomarkers. Thus, there is an imperative need to pinpoint more effective biomarkers to identify patients who may achieve the most benefit from ICIs. The adoption of digital pathology in clinical flow, as being powered by artificial intelligence (AI) especially deep learning, has catalyzed the automated analysis of tissue slides. With the breakthrough of multiplex bioimaging technology, researchers can comprehensively characterize the tumor microenvironment, including the different immune cells’ distribution, function, and interaction. Here, we briefly summarize recent AI studies in digital pathology and share our perspective on emerging paradigms and directions to advance the development of immunotherapy biomarkers.