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Lung Cancer Risk Prediction Models and Racial Disparities

By: Cordi Craig, MS
Posted: Thursday, June 17, 2021

Although Black individuals with a high risk of lung cancer may achieve a greater benefit from annual lung cancer screening compared with White individuals, they tend to be less often eligible for screening through the U.S. Preventive Services Task Force (USPSTF) criteria. Existing risk prediction models are based on data that include fewer than 5% Black participants. According to a research letter published in JAMA Network Open, the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial modified logistic regression model (PLCOm2012) may improve sensitivity in lung cancer detection, especially for Black individuals.

“This work is an important step to reducing disparities in the screening and early detection of lung cancer and making sure we can trust our models to predict those individuals at the highest risk,” Julie A. Barta, MD, of Thomas Jefferson University, Philadelphia, stated in an institutional press release.

To identify differences in the PLCOm2012 lung cancer risk among USPSTF-eligible lung cancer–screened patients, the researchers extracted sociodemographic and clinical data from Black (n = 545) and White (n = 731) individuals.

Lung cancer risk scores did not appear to align with lung cancer diagnoses in Black patients, indicating clinicians should use caution when applying risk models to diverse populations. In the screening cohort, Black individuals had a significantly higher risk of lung cancer than White individuals (P < .001). However, more Black individuals were grouped in lower-risk quartiles than White individuals. Nearly 60% of Black individuals had risk scores in quartiles 3 and 4 compared with 42.5% of White individuals. Yet 11 White individuals (61.1%) with screen-detected lung cancer had a risk score in quartiles 3 and 4 compared with 5 Black individuals (35.7%).

“Further research on comprehensive risk prediction for underrepresented racial and ethnic populations should prioritize diversity and focus on additional factors related to socioeconomic status, geographic variables, the environment, and exposure history,” the researchers concluded.

Disclosure: For full disclosures of the study authors, visit jamanetwork.com.



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