The EU-funded I3LUNG Project: Integrative Science, Intelligent Data Platform for Individualized LUNG Cancer Care With Immunotherapy

医学 肺癌 观察研究 生物标志物 生活质量(医疗保健) 免疫疗法 欧洲联盟 癌症 重症监护医学 肿瘤科 内科学 护理部 业务 生物化学 经济政策 化学
作者
Arsela Prelaj,Monica Ganzinelli,Francesco Trovò,Laila C. Roisman,Alessandra Pedrocchi,Sokol Kosta,Marcello Restelli,Emilia Ambrosini,Massimo Bellini,Gabriella Pravettoni,Dario Monzani,Alessandro Nuara,Ramon Amat,Nikos Spathas,Michael C. Willis,Alexander T. Pearson,James M. Dolezal,Laura Mazzeo,Sabina Sangaletti,Ana M. Correa,Alfonso Aguarón,Iris Watermann,Crina Popa,Giulia Raimondi,Tiziana Triulzi,Stefan Steurer,Giuseppe Lo Russo,Helena Linardou,Nir Peled,Enriqueta Felip,Martin Reck,Marina Chiara Garassino
出处
期刊:Clinical Lung Cancer [Elsevier BV]
卷期号:24 (4): 381-387 被引量:2
标识
DOI:10.1016/j.cllc.2023.02.005
摘要

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. I3LUNG 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.

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