Abstract
Background
Lung cancer (LC) is the leading cause of cancer-related deaths globally. The LUCIA project (Agreement n° 101096473) is dedicated to advance LC screening and prevention by discovering and understanding risk factors contributing to LC development. We use digital tools and AI to integrate multimodal data from wide-population and precision screening cohorts. Linking disparate data layers, we aim to develop a validated risk engine for more accurate, individualized risk stratification in LC screening programs.
Methods
LUCIA is a multinational study integrating retrospective Electronic Health Record (EHR) cohorts with a prospective clinical validation study. We included >50,000 individuals from longitudinal EHR datasets for the retrospective study and 3,029 participants in the prospective study enrolled in 3 phases. We use data harmonization to the OMOP Common Data Model, advanced AI modeling using Time-Aware Transformers (STraTS) for clinical time-series and hybrid models for environmental/geospatial data fusion. As intervention, we apply non-invasive sensing devices. Our primary outcomes include model predictive performance (AUROC/AUPRC) for LC risk, diagnostic sensitivity/specificity of sensing technologies, and identification of implementation.
Results
Preliminary results demonstrate a high predictive and diagnostic performance across the LUCIA toolbox. We have validated a multimodal risk engine integrating clinical and environmental data to achieve high predictive accuracy (AUROC 0.75), outperforming traditional screening models. By demonstrating that non-invasive breath and wearable sensors provide effective triage with 80–87% sensitivity, we support a low-cost primary care screening layer to reduce unnecessary imaging. These technical achievements are backed by the harmonization of 90% of consortium data to OMOP standards.
Conclusions
LUCIA has transitioned a holistic AI toolbox to the public health domain to enable proactive, precision LC prevention.
Key messages
• The LUCIA project (2023-2026) works to validate risk factors beyond smoking history in lung cancer development and to enable proactive, precision lung cancer prevention.
• The LUCIA project results demonstrate the effectiveness of non-invasive breath and wearable sensors and support a low-cost primary care screening layer to reduce unnecessary imaging.