Patricia Rivera, MD, ATSF
President, American Thoracic Society; University of Rochester; Rochester, NY
Session Description
Lung cancer is the leading cause of cancer death in men and women in many countries. While cigarette smoking remains the most important risk factor in the development of lung cancer, there is significant heterogeneity in risk due to sociodemographic factors, occupational and environmental exposures, and underlying concomitant diseases. Lung cancer rates in women in the US have surpassed rates in men, and the incidence of lung cancer continues to rise among people who have never smoked or who haven’t smoked in many years. Risk prediction models can help identify individuals at increased risk of lung cancer; limitations in their use include a lack of data from underrepresented minority populations, resulting in underestimated risk. Using low-dose computed tomography (LDCT), artificial intelligence tools have the potential to predict lung cancer risk without requiring individual clinical or lung nodule characteristics. These tools also hold promise in better-assigning cancer probability in indeterminate lung nodules.
Learning Objectives
At the end of this presentation, attendees will be able to:
- Describe the benefits and limitations of risk prediction models in lung cancer.
- Understand the role of AI in lung cancer risk assessment.
- Understand the role of AI in the evaluation of lung nodules.
CanMEDS Roles: Collaborator, Health Advocate, Scholar
