Increasing Diverse Women's Awareness of and Participation in Lung Cancer Screening
Limited patient awareness and patient provider communication about lung cancer screening could contribute to low screening uptake among women. The purpose of this mixed methods study is to develop a model for lung cancer screening awareness participation among diverse women defined by race and socioeconomic status. Capitalizing upon an existing community-based project, the Cancer Care Equity Program, and a cohort based at a local federally qualified health center, we propose to: 1) Assess demographic, psychosocial, and health system characteristics associated with lung cancer risk perception, screening awareness, intentions and utilization 2) Determine preferences for lung cancer and lung cancer screening educational materials and methods with attention to specific subgroups.
Lung Cancer Risk and Early Detection in Boston Firefighters
In partnership with the Boston Fire Department, and leveraging a recent award they received to fund cancer screening among firefighters, we propose to: 1) Compare radiologic findings on low-dose CT (LDCT) between firefighters and non-firefighters matched for age and sex; 2) Examine the association between self-reported occupational exposures, use of safety equipment, and smoking with LDCT radiologic findings; 3) Investigate the ability of a proteomic assay to identify individuals at high risk of lung cancer among firefighters with suspicious LDCT findings. Our goal is to increase lung cancer early detection among firefighters and identify a high-risk subset. More information is available at https://www.firehealthstudy.org/
Improving Lung Cancer Risk Stratification and Expanding Access to Lung Cancer Screening
Lung cancer is the leading cause of cancer-related deaths worldwide. While smoking is the leading cause of lung cancer, lung cancer rates among people that never smoked are on the rise. However, current guidelines for lung cancer screening via low-dose CT only consider age and smoking history for eligibility. We believe that every person has the right to know their risk of developing cancer — Sybil is a deep learning model that accurately predicts a patient’s risk of lung cancer up to six years in advance from analyzing a low-dose CT scan, ensuring that lung cancer is detected in its earliest stages. As part of a team of scientists and clinicians across Mass General Brigham, Massachusetts Institute of Technology, and the world, we are learning how to use Sybil to identify people at increased risk for lung cancer, including people that do not currently qualify for lung cancer screening. Learn more about ongoing research projects here. Watch a video to learn more about Sybil here.
