Summary
Immunotherapy (IO) is the new standard of care for many patients with advanced Non-Small Cell Lung Cancer (aNSCLC), yet only around 30-50% of treated patients benefit from IO in the long term. Programmed Death-Ligand 1 (PD-L1) remains the only biomarker used to predict patient outcome to IO, though its efficacy is limited. Other potential biomarkers have been identified, yet not validated in prospective randomized clinical trials, providing only partial evidence. Due to the dynamic complexity of the immune system-tumour microenvironment, its interaction with the host and patient behaviour, it?s unlikely for a single biomarker to accurately predict patient outcome. Artificial Intelligence (AI) and machine learning (ML) frameworks, that synthetize and correlate information from multiple sources, are essential to develop powerful decision-making tools able to deal with this highly complex context and provide individualized predictions to improve patient outcomes reducing the economic burden of health care systems in NSCLC.
The aim of the I3LUNG project is to develop such AI-based tools to assist in improving survival and quality of life, preventing undue toxicity, and reducing treatment costs. I3LUNG adopts a two-pronged approach: setting up a transnational platform of available data from 2000 patients in order to validate the AI models, and generating a multi-omics prospective data collection in 200 NSCLC patients integrating diverse -omic information then validate its usefulness in leading IO therapeutic decisions. A psychological study will help in defining the impact of AI-guided decisions on patients, eliciting their preference, and physicians comparing AI with Human Intuition. The final goal is the construction of a novel integrated AI-assisted Data Storage and Elaboration Platform backed up by Trustworthy Explainable AI methodology, ensuring its accessibility and ease of use by healthcare providers and patients alike.
The aim of the I3LUNG project is to develop such AI-based tools to assist in improving survival and quality of life, preventing undue toxicity, and reducing treatment costs. I3LUNG adopts a two-pronged approach: setting up a transnational platform of available data from 2000 patients in order to validate the AI models, and generating a multi-omics prospective data collection in 200 NSCLC patients integrating diverse -omic information then validate its usefulness in leading IO therapeutic decisions. A psychological study will help in defining the impact of AI-guided decisions on patients, eliciting their preference, and physicians comparing AI with Human Intuition. The final goal is the construction of a novel integrated AI-assisted Data Storage and Elaboration Platform backed up by Trustworthy Explainable AI methodology, ensuring its accessibility and ease of use by healthcare providers and patients alike.
Unfold all
/
Fold all
More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101057695 |
| Start date: | 01-06-2022 |
| End date: | 31-05-2027 |
| Total budget - Public funding: | 9 996 697,50 Euro - 9 996 695,00 Euro |
Cordis data
Original description
Immunotherapy (IO) is the new standard of care for many patients with advanced Non-Small Cell Lung Cancer (aNSCLC), yet only around 30-50% of treated patients benefit from IO in the long term. Programmed Death-Ligand 1 (PD-L1) remains the only biomarker used to predict patient outcome to IO, though its efficacy is limited. Other potential biomarkers have been identified, yet not validated in prospective randomized clinical trials, providing only partial evidence. Due to the dynamic complexity of the immune system-tumour microenvironment, its interaction with the host and patient behaviour, it?s unlikely for a single biomarker to accurately predict patient outcome. Artificial Intelligence (AI) and machine learning (ML) frameworks, that synthetize and correlate information from multiple sources, are essential to develop powerful decision-making tools able to deal with this highly complex context and provide individualized predictions to improve patient outcomes reducing the economic burden of health care systems in NSCLC.The aim of the I3LUNG project is to develop such AI-based tools to assist in improving survival and quality of life, preventing undue toxicity, and reducing treatment costs. I3LUNG adopts a two-pronged approach: setting up a transnational platform of available data from 2000 patients in order to validate the AI models, and generating a multi-omics prospective data collection in 200 NSCLC patients integrating diverse -omic information then validate its usefulness in leading IO therapeutic decisions. A psychological study will help in defining the impact of AI-guided decisions on patients, eliciting their preference, and physicians comparing AI with Human Intuition. The final goal is the construction of a novel integrated AI-assisted Data Storage and Elaboration Platform backed up by Trustworthy Explainable AI methodology, ensuring its accessibility and ease of use by healthcare providers and patients alike.
Status
SIGNEDCall topic
HORIZON-HLTH-2021-CARE-05-02Update Date
09-02-2023
Images
No images available.
Geographical location(s)
Structured mapping
Unfold all
/
Fold all
Search
Organisations
-
| FONDAZIONE IRCCS ISTITUTO NAZIONALE DEI TUMORI (Coördinator)
-
| AALBORG UNIVERSITET
-
| FUNDACIO PRIVADA INSTITUT D'INVESTIGACIO ONCOLOGICA DE VALL-HEBRON (VHIO)
-
| IHE, INSTITUTET FOR HALSO- OCH SJUKVARDSEKONOMI AKTIEBOLAG
-
| ISTITUTO DI RICERCHE FARMACOLOGICHE MARIO NEGRI
-
| ISTITUTO EUROPEO DI ONCOLOGIA SRL
-
| KATHOLIEKE UNIVERSITEIT LEUVEN
-
| LUNG CANCER EUROPE LUCE
-
| LUNGENCLINIC GROSSHANSDORF GMBH
-
| MEDICA SCIENTIA INNOVATION RESEARCH SL
-
| ML CUBE S.R.L.
-
| PERSEYS ANONYMOS ETAIREIA EKMETALLEFSIS LEITOYRGEIAS FOREON YGEIONOMIKIS MERIMNAS
-
| Politecnico di Milano
-
| SHAARE ZEDEK MEDICAL CENTER
-
| THE UNIVERSITY OF CHICAGO