Antonio Jesus Diaz Honrubia

Ph.D.

Universidad Politécnica de Madrid

Associate Professor (tenured)

NLST (Learn more about this study)

NLST-1273

Jun 17, 2024

AI-Enhanced Exploration of Lung Cancer Risk Factors

Lung cancer poses a significant global public health challenge, with millions of new cases diagnosed annually and alarmingly low survival rates. Despite existing screening efforts, timely and accurate diagnosis remains elusive. The research project is founded on the belief that through comprehensive representation and analysis of multimodal data using advanced AI algorithms, including Machine Learning and Deep Learning, we can enhance our understanding of lung cancer etiology. This advancement holds the potential to improve screening programs and develop more effective risk prediction models, refining patient stratification, identifying risk factors, and predicting lung cancer risk more accurately based on clinical data.
To achieve the research objectives, we will develop advanced lung cancer screening methods by integrating structured clinical data and utilizing advanced analytics. We’ll also refine existing case detection algorithms and improve risk estimation models through systematic analysis of structured data sets and advanced AI techniques. Additionally, we’ll conduct detailed investigations to gain deeper insights into tumor progression, focusing on structured data related to tumor characteristics and patient outcomes. Through these efforts, we aim to detect and understand key risk factors associated with lung cancer, leading to improved clinical outcomes.

- Develop knowledge representation and advanced AI models for lung cancer risk prediction, improving screening and interventions for better patient outcomes.

- Understand underlying risk factors associated with lung cancer development, utilizing predictive model insights and analytical findings.

- Gain comprehensive insights into lung cancer tumor microenvironment diversity, identifying patterns linked to disease progression.