Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.
Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain.
Clin Cancer Res. 2023 Jan 17;29(2):316-323. doi: 10.1158/1078-0432.CCR-22-0390.
Immunotherapy by immune checkpoint inhibitors has become a standard treatment strategy for many types of solid tumors. However, the majority of patients with cancer will not respond, and predicting response to this therapy is still a challenge. Artificial intelligence (AI) methods can extract meaningful information from complex data, such as image data. In clinical routine, radiology or histopathology images are ubiquitously available. AI has been used to predict the response to immunotherapy from radiology or histopathology images, either directly or indirectly via surrogate markers. While none of these methods are currently used in clinical routine, academic and commercial developments are pointing toward potential clinical adoption in the near future. Here, we summarize the state of the art in AI-based image biomarkers for immunotherapy response based on radiology and histopathology images. We point out limitations, caveats, and pitfalls, including biases, generalizability, and explainability, which are relevant for researchers and health care providers alike, and outline key clinical use cases of this new class of predictive biomarkers.
免疫检查点抑制剂的免疫疗法已成为许多类型实体瘤的标准治疗策略。然而，大多数癌症患者不会对此治疗产生反应，因此预测对该治疗的反应仍然是一个挑战。人工智能 (AI) 方法可以从复杂数据（如影像数据）中提取有意义的信息。在临床常规中，放射学或组织病理学影像普遍可用。已经使用 AI 从放射学或组织病理学影像中直接或间接地通过替代标志物来预测免疫疗法的反应。虽然这些方法目前都未在临床常规中使用，但学术和商业的发展都指向了在不久的将来可能在临床上采用。在这里，我们总结了基于放射学和组织病理学影像的免疫疗法反应的基于 AI 的影像生物标志物的最新进展。我们指出了包括偏差、可泛化性和可解释性等限制、注意事项和陷阱，这些对研究人员和医疗保健提供者都很重要，并概述了这一类新的预测生物标志物的关键临床应用案例。
