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Active NON-SBIR/STTR RPGS NIH (US)

Addressing racial disparities in lung cancer screening

$5.47M USD

Funder NATIONAL CANCER INSTITUTE
Recipient Organization Vanderbilt University Medical Center
Country United States
Start Date Apr 05, 2021
End Date Mar 31, 2026
Duration 1,821 days
Number of Grantees 2
Roles Principal Investigator; Co-Investigator
Data Source NIH (US)
Grant ID 10897719
Grant Description

PROJECT SUMMARY/ABSTRACT Screening promotes early detection of cancer to decrease mortality. Unfortunately, significant racial disparities exist in lung cancer screening. Recently published findings by our team show that under current national screening guidelines African Americans have reduced eligibility for lung cancer screening compared to whites.

These screening guidelines are based on a combination of age and smoking pack-year criteria derived from a national lung screening trial that was primarily (91%) white. Importantly, smoking behaviors and baseline risks for lung cancer differ greatly between African Americans and whites. Because of this, a risk-based screening

strategy may provide a more equitable assessment of eligibility than current screening guidelines. However, the development of personalized risk prediction models for lung cancer in African Americans has been limited. To address this gap and to improve equity in screening eligibility, we propose building a personalized prediction tool

using the combined data from three large-scale population-based prospective cohorts with substantial African American representation. The combined cohorts have over 336,000 individuals (44% African American) and 9,132 incident lung cancer cases from across the United States. We propose the following three aims: 1)

construct a well-calibrated natural-history model of lung cancer risk for screening in African Americans, 2) evaluate lung cancer screening strategies by simulation and identify sub-populations who would benefit from screening, accounting for comorbidities and false-positives, and 3) develop a web-based decision aid for

screening that is culturally appropriate. We will employ innovative machine learning techniques and state-of-the- art statistical methods to build a well-calibrated lung cancer prediction model for African Americans. Careful examination will identify sub-populations (such as women, low socioeconomic status, rural, age groups, etc.)

that will benefit from screening. A key innovative aspect of this proposal is its community-engaged approach and partnership with a Community Advisory Board, both of which will help translate our empirical findings into the design of a patient-oriented decision aid. This project is relevant to the mission of the National Cancer Institute

since it focuses on establishing equity in lung cancer screening eligibility. Our findings will have sustained impact on precision health and motivate improved clinical strategies for the early detection of lung cancer for African Americans.

All Grantees

Vanderbilt University Medical Center

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