Predictive risk model found effective to detect lung cancer
New research from Canada may lead oncologists to a new method of detecting lung cancer.
A recent study published in the The Lancet suggests a low-dose CT scan for high-risk patients selected from high predictive risk model criteria is more effective in detecting early signs of lung cancer than using criteria similarly used in the National Lung Screening Trial (NLST).
Researchers designed the study, denoted as the Pan-Canadian Early Detection of Lung Cancer (PanCan) study, to evaluate the effectiveness of a high-risk prediction model in detecting lung cancer.
"Low-dose CT lung cancer screening is most effective when applied to high-risk individuals. [U.S. Preventive Services Task Force] entry criteria do not quantify risk, and a sizeable proportion of Americans diagnosed with lung cancer fail to meet the USPSTF screening entry criteria," said Martin C. Tammemagi, PhD, lead author of the study.
This single-arm, prospective study recruited current and former smokers between 50 and 75 years of age from eight different cities across Canada to undergo a low-dose CT scan of the lungs. Roughly 2,337 eligible "ever-smokers" were recruited between Sept. 24, 2008, and Dec. 17, 2010, none having a recorded history of lung cancer. However, according to the study, participants were required to have a have a six-year risk of lung cancer of at least 2 percent, as determined by the PanCan model.
A five-year follow-up was conducted for all participants, concluding the study officially in 2015. Researchers found 164 individuals were diagnosed with lung cancer.
Tammemagi and his colleagues concluded that a low-dose CT-scan on high risk individuals is effective in detecting early and curable signs of lung cancer in high-risk patients. A more accurate selection of individuals who are at a high risk for lung cancer could also improve cost effectiveness.
"The results of the study support the notion that selecting individuals for lung cancer on the basis of a highly predictive risk model is more effective than using NLST-like criteria," Tammemagi said.
A recent graduate from Dominican University (IL) with a bachelor’s in journalism, Melissa joined TriMed’s Chicago team in 2017 covering all aspects of health imaging. She’s a fan of singing and playing guitar, elephants, a good cup of tea, and her golden retriever Cooper.