A new study led by scientists from the International Agency for Research on Cancer (IARC) compared the performance of lung cancer risk prediction models against the criteria currently used to determine eligibility for lung cancer screening in the United States. Reseatchers found that all the included risk-based strategies achieved better average estimated screening efficiency than the current criteria, and also reduced racial and ethnic differences in efficiency. The study was published by Feng et al in Annals of Internal Medicine.
The researchers assessed 16 lung cancer risk models in nearly 642,000 Asian, Hispanic, non-Hispanic Black, and non-Hispanic White adults aged 50 to 80 years with a history of smoking. They examined how well the models could predict lung cancer risk and their impact on screening eligibility, sensitivity, and efficiency across the different racial and ethnic groups. When each model selected the same number of individuals for screening as the United States Preventive Services Task Force criteria (USPSTF-2021), all models performed more efficiently overall and reduced racial and ethnic differences in screening efficiency compared with USPSTF-2021. However, the existing risk models—which were developed using data from mainly non-Hispanic White individuals—tended to underestimate risk in non-Hispanic Black patients and had reduced predictive ability in Asians and non-Hispanic Black patients.
These findings suggest that using risk prediction models for lung cancer screening eligibility could yield a better balance of benefits and harms, with smaller differences across racial and ethnic groups. However, further research is needed to optimize prediction models for the diverse population in the United States.
DISCLOSURE: For full disclosures of the study authors, visit acpjournals.org.
