Though several large-scale trials such as the National Lung Screening Trial have demonstrated clear benefits from CT lung screening, guidelines drawn from these findings have limited the screening exam to individuals with a smoking history.
Yet roughly 25% of lung cancer cases occur in individuals who have never smoked, and this proportion is considerably larger for Asian women; about 60% to 80% of female lung cancer patients in Asia are never-smokers, noted study co-authors I-Shou Chang, PhD, and Chao Hsiung, PhD, of the Taiwanese National Health Research Institutes, and colleagues.
Recently, various studies have shown that integrating risk-prediction models into CT lung screening eligibility criteria could help boost the efficiency of screening and prevent more cancer deaths than relying on the U.S. Preventive Services Task Force (USPSTF) guidelines.
In light of these findings, Chang, Hsiung, and colleagues developed a risk-prediction model, called the Taiwan Never-Smoking Female risk model, capable of identifying individuals with a 1.51% or greater risk of developing lung cancer within six years. The model considered various factors to determine risk, including family history of lung cancer, history of chronic obstructive pulmonary disorder, education level, and genetic information (i.e., the presence of 11 single nucleotide polymorphisms), among others.
They applied their risk model to the data of 8,283 female never-smokers between the ages of 55 and 70, drawn from the Genetic Epidemiology Study of Lung Adenocarcinoma in Taiwan and the Taiwan Biobank before 2016.
The risk prediction model estimated that 3.94% of female never-smokers in the Taiwan Biobank from 2016 to 2018 would develop lung cancer, with an area under the receiver operating characteristic curve of 0.71.
When applied to the Lung Cancer Pharmacogenomics study, the risk model identified 27% of the female never-smokers who developed lung cancer within six years, the authors found. None of these lung cancer patients would have been eligible for screening based on the USPSTF screening guidelines.
The risk-prediction model demonstrated good discriminative power overall and demonstrated the potential to identify female Asian never-smokers for CT lung screening, the researchers noted.
"Our study may be useful for policymakers in screening program design. ... It might also be useful for [female never-smokers] or their doctors to get some idea about their risks for lung cancer and decide if they might benefit from CT lung cancer screening," they wrote.
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