Lung cancer screening: How best to gauge effectiveness?
February 21, 2013 | by Tami Dennis
Perhaps we should use additional risk factors – not just the ones currently used – when selecting whom to screen for lung cancer, suggest researchers in a new study.
In research published in the Feb. 21 issue of New England Journal of Medicine, scientists compared the results of two models for lung cancer prediction. One was based on the National Lung Screening Trial, or NLST. The other was based on what’s known as the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, helpfully referred to as PLCO.
NLST had found that screening with low-dose CT was an effective way to reduce lung cancer mortality – and in a cost-effective way. PLCO went a step further in its effort to screen patients, adding level of education, family history of lung cancer and whether the patient had chronic obstructive pulmonary disease, among other factors.
The researchers compared data from the two trials and found, in short, that the more restrictive criteria would be better at detecting lung cancer without a loss of specificity.
“Because the mortality reduction from CT screening effectiveness did not vary according to lung-cancer risk, it appears that use of the PLCO to select persons for lung-screening programs could potentially be an effective method leading to improved cost-effectiveness of screening with additional deaths from lung cancer prevented,” they concluded.
City of Hope’s Dan Raz M.D. , supports this conclusion, up to a point. As he told MedPage Today: “The way the criteria were established for the NLST was not based on scientific fact. They were trying to come up with criteria that would result in a positive study ... and rightfully so.”
But Raz, a proponent of early screening for people at high risk of lung cancer, thinks that the PLCO model is just an "important step. That doesn't mean that's where screening should stop."
Other data might actually be more useful, he says. Like many researchers, he wants to explore that data to create the most effective criteria.
His goal in short: “Screen people more effectively.”