Zhang Jessica, Choi Horyun, Kim Yeseul, Park Jonghanne, Cho Sukjoo, Kim Eugene, Cho Allen, Lee Ju Young, Choi Jaeyoun, Low Christmann, Jung Chan Mi, Yu Emma J, Chuang Jeffrey H, Cooper Lee, Chae Young Kwang
Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
Department of Medicine, University of Hawaii, Honolulu, Hawaii, USA.
J Immunother Cancer. 2025 Aug 4;13(8):e011346. doi: 10.1136/jitc-2024-011346.
Immuno-oncology and the advent of immunotherapies, in particular immune checkpoint inhibitors (ICIs), have fundamentally altered the way we treat cancer. Yet only a small subset of patients actually responds to ICIs, and many face significant adverse effects, making the accurate selection of patients for ICIs essential to the work of immuno-oncology. Immune biomarkers, such as programmed death-ligand 1, microsatellite instability/defective mismatch repair, and tumor mutational burden have been developed for patient selection and stratification for ICIs, though their predictive abilities remain limited. This is due to several challenges: lack of adequate tissue sampling, the time-consuming and subjective nature of manual visual-based quantification techniques, and the growing recognition of the complexity of the tumor microenvironment, for which these tests cannot fully capture on their own. Meanwhile, emerging technologies in the field of artificial intelligence (AI), such as the performance of deep learning techniques in digital pathology, have garnered significant attention for their potential to be used in this space. Many have now turned their attention towards the immuno-oncology-related applications for digital pathology, particularly in analyzing whole-slide images of widely available H&E-stained slides to aid in immune biomarker detection and ICI response prediction. In this review, we discuss the current landscape of AI-based digital pathology in immuno-oncology, including its applications for identifying and measuring immune biomarkers and, importantly, its potential for predicting ICI response and survival outcomes. We will end by discussing the challenges and future directions of adopting AI technologies for clinical deployment.
免疫肿瘤学以及免疫疗法的出现，尤其是免疫检查点抑制剂（ICI），从根本上改变了我们治疗癌症的方式。然而，只有一小部分患者实际对ICI有反应，而且许多患者面临严重的不良反应，因此准确选择适合ICI治疗的患者对于免疫肿瘤学工作至关重要。免疫生物标志物，如程序性死亡配体1、微卫星不稳定性/错配修复缺陷以及肿瘤突变负荷，已被开发用于ICI治疗的患者选择和分层，尽管它们的预测能力仍然有限。这是由于几个挑战：缺乏足够的组织样本、基于手动视觉量化技术的耗时性和主观性，以及对肿瘤微环境复杂性的认识不断提高，而这些测试本身无法完全捕捉到这些复杂性。与此同时，人工智能（AI）领域的新兴技术，如深度学习技术在数字病理学中的应用，因其在该领域的应用潜力而备受关注。现在许多人将注意力转向了数字病理学在免疫肿瘤学方面的相关应用，特别是在分析广泛可用的苏木精和伊红（H&E）染色玻片的全切片图像以辅助免疫生物标志物检测和ICI反应预测方面。在这篇综述中，我们讨论了免疫肿瘤学中基于AI的数字病理学的现状，包括其在识别和测量免疫生物标志物方面的应用，以及重要的是，其预测ICI反应和生存结果的潜力。我们将通过讨论采用AI技术进行临床部署的挑战和未来方向来结束本文。
