Reporting from the UK: Machine Learning Can Better ID Who Needs Lung Cancer Screening

A machine learning model that uses only data on age, smoking duration, and pack-years can predict lung cancer risk and identify who needs lung cancer screening with better performance than currently used methods, according to a study published online Oct. 3 in PLOS Medicine.

Thomas Callender, Ph.D., from University College London, and colleagues developed and validated ensemble machine learning models to determine eligibility for risk-based lung cancer screening. The model was developed using data from 216,714 ever-smokers participating in the U.K. Biobank prospective cohort and 26,616 high-risk ever-smokers participating in the U.S. National Lung Screening randomized controlled trial. Validation used data from 49,593 participants in the chest radiography arm and all 80,659 ever-smoking participants in the U.S. Prostate, Lung, Colorectal, and Ovarian Screening Trial.

Source: Advances and More licensed by HealthDay