Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), 20133 Milan, Italy; Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy.
Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), 20133 Milan, Italy.
Clin Lung Cancer. 2023 Jun;24(4):381-387. doi: 10.1016/j.cllc.2023.02.005. Epub 2023 Feb 28.
Although immunotherapy (IO) has changed the paradigm for the treatment of patients with advanced non-small cell lung cancers (aNSCLC), only around 30% to 50% of treated patients experience a long-term benefit from IO. Furthermore, the identification of the 30 to 50% of patients who respond remains a major challenge, as programmed Death-Ligand 1 (PD-L1) is currently the only biomarker used to predict the outcome of IO in NSCLC patients despite its limited efficacy. Considering the dynamic complexity of the immune system-tumor microenvironment (TME) and its interaction with the host's and patient's behavior, it is unlikely that a single biomarker will accurately predict a patient's outcomes. In this scenario, Artificial Intelligence (AI) and Machine Learning (ML) are becoming essential to the development of powerful decision-making tools that are able to deal with this high-complexity and provide individualized predictions to better match treatments to individual patients and thus improve patient outcomes and reduce the economic burden of aNSCLC on healthcare systems. ILUNG is an international, multicenter, retrospective and prospective, observational study of patients with aNSCLC treated with IO, entirely funded by European Union (EU) under the Horizon 2020 (H2020) program. Using AI-based tools, the aim of this study is to promote individualized treatment in aNSCLC, with the goals of improving survival and quality of life, minimizing or preventing undue toxicity and promoting efficient resource allocation. The final objective of the project is the construction of a novel, integrated, AI-assisted data storage and elaboration platform to guide IO administration in aNSCLC, ensuring easy access and cost-effective use by healthcare providers and patients.
虽然免疫疗法(IO)改变了治疗晚期非小细胞肺癌(aNSCLC)患者的模式,但只有约 30%至 50%的接受治疗的患者能从 IO 中获得长期获益。此外,识别出能从中获益的 30%至 50%的患者仍然是一个主要挑战,因为尽管程序性死亡配体 1(PD-L1)的疗效有限,但它目前是唯一用于预测 NSCLC 患者 IO 结果的生物标志物。考虑到免疫系统-肿瘤微环境(TME)及其与宿主和患者行为相互作用的动态复杂性,不太可能有一种单一的生物标志物能准确预测患者的结果。在这种情况下,人工智能(AI)和机器学习(ML)对于开发强大的决策工具变得至关重要,这些工具能够处理这种高复杂性,并提供个性化的预测,以更好地将治疗方法与个体患者匹配,从而改善患者的结果并减轻 aNSCLC 对医疗保健系统的经济负担。ILUNG 是一项针对接受 IO 治疗的 aNSCLC 患者的国际性、多中心、回顾性和前瞻性观察研究,完全由欧盟在 Horizon 2020(H2020)计划下资助。该研究利用基于 AI 的工具,旨在促进 aNSCLC 的个体化治疗,目标是改善生存和生活质量,最大限度地减少或预防不必要的毒性,并促进资源的有效分配。该项目的最终目标是构建一个新的、集成的、AI 辅助的数据存储和处理平台,以指导 aNSCLC 中的 IO 管理,确保医疗保健提供者和患者易于访问和具有成本效益的使用。