Novel Predictive Model More Effectively Identifies Risk for Lung Cancer

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Existing methodologies for early detection of lung cancer have decreased mortality, but some patients eventually diagnosed with lung cancer don't meet screening entry criteria.
Existing methodologies for early detection of lung cancer have decreased mortality, but some patients eventually diagnosed with lung cancer don't meet screening entry criteria.

A novel predictive risk model more effectively identified patients at risk of developing lung cancer than other previously established models, according to a study published in The Lancet Oncology.

Although the current methodologies for early detection of lung cancer — including models such as the National Lung Screening Trial (NLST) — have been effective in decreasing mortality, many patients with eventually diagnosed lung cancer do not meet the established criteria for screening entry.


In the prospective Pan-Canadian Early Detection of Lung Cancer (PanCan) study, investigators recruited 2537 patients who were current or former smokers (ever-smokers) and who did not have a self-reported history of lung cancer. Eligible study patients must have had a 2% 6-year risk of lung cancer as estimated by the PanCan model.

The incidence of lung cancer was significantly greater among patients who were screened during the PanCan study (6.5%) compared to patients observed in the NLST (4.0%) (P <.0001).


At the time of median follow-up of 5.5 years, 172 lung cancers were diagnosed in 164 screened patients, resulting in a cumulative incidence of 0.065 (95% CI, .055-.075) and an incidence rate of 138.1 per 10,000 person years.  

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