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Patient populations in lung cancer screening models vary widely

The population of ever smokers in the United States chosen to be screened for lung cancer by nine risk models vary widely, according to research published in Annals of Internal Medicine.

“Lung cancer screening guidelines recommend using individualized risk models to refer ever-smokers for screening,” Hormuzd A. Katki, PhD, from the National Cancer Institute, and colleagues wrote. “However, different models select different screening populations. The performance of each model in selecting ever-smokers for screening is unknown.”

Katki and colleagues assessed nine lung cancer risk models to compare the populations selected for each model and to investigate their predictive performance in two cohorts of ever smokers.

The models examined included the Bach model, the Spitz model, the Liverpool Lung Project (LLP) model, the LLP Incidence Risk Model (LLPi), the Hoggart model, the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Model 2012 (PLCOM2012), the Pittsburgh Predictor, the Lung Cancer Risk Assessment Tool (LCRAT) and the Lung Cancer Death Risk Assessment Tool (LCDRAT).

The researchers found that screening populations varied greatly among the nine lung cancer risk models, ranging from 7.6 million to 26 million ever-smokers. Additionally, the models showed no agreement on who to select for lung cancer screening.

These variations were due to differing predictive performance among the models, according to the researchers. The Bach model, PLCOM2012, LCRAT and LCDRAT were the best performing models and were better calibrated (expected–observed ratio range, 0.92-1.12) and had higher AUCs (range, 0.75-0.79) compared with the remaining models which overestimated risk (expected–observed ratio range, 0.83-3.69) and had lower AUCs (range, 0.62-0.75).

These best performing models chose similar screening populations of ever-smokers, ranging from 7.6 million to 10.9 million. The models also agreed on 73% of individuals selected.

“Ending the epidemic of smoking-related illness requires continued progress in smoking cessation and prevention,” Katki and colleagues concluded. “Effectively and efficiently targeting lung cancer screening to persons at highest risk can further reduce lung cancer mortality, the leading type of cancer death. Our findings suggest that four lung cancer risk models perform best in selecting U.S. ever-smokers for screening. The models should be further refined to improve their performance in certain subpopulations.”

In an accompanying editorial, Martin C. Tammemägi, DVM, MSc, PhD, from Brock University, St. Catharines, Ontario, Canada, wrote that implementing risk prediction models for lung cancer screening requires several issues to be addressed first, such as convincing policymakers to use risk models to identify screening eligible individuals, identifying high risk thresholds and establishing ideal methods to implement the models, among others.

“Lung cancer screening is rapidly evolving,” he wrote. “Over the next few years, many of the aforementioned issues are likely to be addressed and lung cancer screening is likely to improve.” – by Alaina Tedesco

Disclosure: Katki reports no relevant financial disclosures. Please see study for all other authors’ relevant financial disclosures. Tammemägi reports being a developer of the PLCOm2012 lung cancer risk prediction model.