AI-driven Analysis of Risk Factors in Lung Cancer Development
Principal Investigator
Name
Antonio Jesus Diaz Honrubia
Degrees
Ph.D.
Institution
Universidad Politécnica de Madrid
Position Title
Associate Professor (tenured)
Email
antoniojesus.diaz@upm.es
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-1593
Initial CDAS Request Approval
Jun 17, 2024
Title
AI-driven Analysis of Risk Factors in Lung Cancer Development
Summary
Lung cancer presents a significant global health challenge, necessitating a deeper understanding of its risk factors for effective prevention and management. Our proposed research seeks to leverage advanced artificial intelligence (AI) methodologies to identify and elucidate the complex interplay of factors contributing to lung cancer susceptibility. Through the integration of comprehensive clinical data and sophisticated Knowledge representation and AI models, including Machine Learning and Deep learning techniques, we aim to uncover critical risk factors, personalize predictive models, and improve screening programs for enhanced patient outcomes.
Aims
Our research endeavors encompass two primary aims:
- Development of Advanced AI-driven Lung Cancer Risk Models: our primary objective is to develop knowledge representation and AI-driven models that surpass existing methodologies in predicting lung cancer risk. By integrating diverse datasets and employing state-of-the-art AI techniques, we aim to enhance prediction accuracy and enable the implementation of personalized risk-based screening strategies. These models will provide actionable insights for healthcare professionals and empower individuals with personalized risk assessments, ultimately improving early detection and intervention efforts.
- Illumination of Intricate Risk Factors: in tandem with model development, we seek to illuminate the intricate web of risk factors associated with lung cancer development. By analysing comprehensive clinical data and exploring the relative significance of identified risk factors, we aim to uncover novel or lesser-known factors contributing to disease progression. This endeavour will enhance our understanding of lung cancer across diverse population groups, informing targeted interventions and public health policies.
Collaborators
Alejandro Rodríguez González – Full Professor (CTB-UPM) alejandro.rg@upm.es
Guillermo Antonio Vigueras González – Associate Professor (CTB-UPM) guillermo.vigueras@upm.es
Paloma Tejera Nevado – Postdoctoral Researcher (CTB-UPM) paloma.tejera@upm.es
Lucía Prieto Santamaría – Postdoctoral Researcher (CTB-UPM) lucia.prieto.santamaria@upm.es
Delia Aminta Moreno Perdomo – Predoctoral Researcher (CTB-UPM) da.moreno@upm.es