Project description
AI-based platform to assist in lung cancer immunotherapy
Although immunotherapy is the new standard of care for advanced non-small cell lung cancer (NSCLC), less than 50 % of patients benefit from this treatment in the long term. Programmed death-ligand 1 biomarker is used to predict immunotherapy outcomes with limited efficacy, and other potential biomarkers have not yet been validated in randomised clinical trials. The EU-funded I3LUNG project aims to develop AI-based tools for improving patients’ survival and quality of life. The project will set up a global platform comprising data from 2 000 patients for AI models validation; moreover, it will collect multi-omics data from 200 NSCLC patients for information integration and application in leading immunotherapy decisions.
Objective
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.
Fields of science
Keywords
Programme(s)
- HORIZON.2.1 - Health Main Programme
- HORIZON.2.1.6 - Health Care Systems
Funding Scheme
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinator
20133 Milan
Italy