TY - JOUR
T1 - The EU-funded I3LUNG Project
T2 - Integrative Science, Intelligent Data Platform for Individualized LUNG Cancer Care With Immunotherapy
AU - Prelaj, Arsela
AU - Ganzinelli, Monica
AU - Trovo’, Francesco
AU - Roisman, Laila C.
AU - Pedrocchi, Alessandra Laura Giulia
AU - Kosta, Sokol
AU - Restelli, Marcello
AU - Ambrosini, Emilia
AU - Broggini, Massimo
AU - Pravettoni, Gabriella
AU - Monzani, Dario
AU - Nuara, Alessandro
AU - Amat, Ramon
AU - Spathas, Nikos
AU - Willis, Michael
AU - Pearson, Alexander
AU - Dolezal, James
AU - Mazzeo, Laura
AU - Sangaletti, Sabina
AU - Correa, Ana Maria
AU - Aguaron, Alfonso
AU - Watermann, Iris
AU - Popa, Crina
AU - Raimondi, Giulia
AU - Triulzi, Tiziana
AU - Steurer, Stefan
AU - Lo Russo, Giuseppe
AU - Linardou, Helena
AU - Peled, Nir
AU - Felip, Enriqueta
AU - Reck, Martin
AU - Garassino, Marina Chiara
N1 - Funding Information:
The study has received funding from the European Union's Horizon Europe Framework Programme under Grant Agreement n. 101057695 . We also wish to thank the assistance received from the following collaborators: Roberto Ferrara, Mattia Boeri, Gabriella Sozzi, Mario Paolo Colombo, Giovanni Scoazec, Filippo de Braud, Leonardo Provenzano, Andrea Spagnoletti, Andrea Vingiani, Giancarlo Pruneri, Antonino Belfiore, Settimio Di Gregorio, Annamaria Piva, Luca Agnelli, Fabrizio Baggio, Ermenegilda Gallucci, Claudia Giani, Melissa Fernandez, Simona Ferrante, Roberta Pastorelli, Laura Brunelli, Valter Torri, Giulia de Simoni, Mirko Marabese, Roberto Grasso, Chiara Marzorati, Valeria Sebri, Alessandra Fabbri, Antonia Martinetti, Elisa Sottotetti, Nicola Caporaso, Ana Callejo, Joan Frigola, Caterina Carbonell, Patricia Iranzo, Javier Gonzalo Ruiz, Sandra Porta, Alicia Garcia, Natacha Bonnet, David Molina, Kanelia-Maria Gkiati Renana Ofan, Claudia Proto, Marta Brambilla, Ronald Simon, Anton Vedder, Sofie Fleerackers, Bengi Zeybek, Edith Appelmans, Myriam Wityrouw, Elisabetta Biasin, Andreas Nilsson, Klas Kellerborg, Ulf Persson, Gunnar Brådvik, M Retzer, Elda Tagliabue, Andra Diana Dumitrascu, Rosa Maria, Di Mauro Michele Zanitti, Stefania Vallone, Maeve O'Sulliva, Anne-Marie Baird, Raffaele Califano, Umberto Malapelle, Davide Lipodio.
Publisher Copyright:
© 2023
PY - 2023/6
Y1 - 2023/6
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Machine learning
KW - Non-small cell lung cancer
KW - Personalized medicine
KW - Predictive biomarkers
UR - http://www.scopus.com/inward/record.url?scp=85150767716&partnerID=8YFLogxK
U2 - 10.1016/j.cllc.2023.02.005
DO - 10.1016/j.cllc.2023.02.005
M3 - Journal article
AN - SCOPUS:85150767716
SN - 1525-7304
VL - 24
SP - 381
EP - 387
JO - Clinical Lung Cancer
JF - Clinical Lung Cancer
IS - 4
ER -