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PLCO model more accurate in predicting cancer than NSLT criteria
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A lung cancer risk-prediction model based on data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial was more sensitive than The National Lung Screening Trial enrollment criteria for selecting individuals who were subsequently diagnosed with lung cancer.
The PLCO model also was more efficient at identifying persons for lung cancer screening.
Several organizations, including the American Cancer Society, have used the NLST enrollment criteria for identifying individuals for lung screening, which includes patients aged 55 to 74 years who have a minimum 30-pack-year smoking history and who currently smoke or have quit within the past 15 years. The NLST criteria were intended to increase the yield of lung cancers, but they exclude many known risk factors for lung cancer, and with dichotomization of continuous data, significant valuable information is not included.
Martin C. Tammemägi
“Applying an accurate lung cancer risk prediction model to a population can identify persons at highest risk. Screening them is expected to increase the number of lung cancers identified per given sample size or reduce the number of persons needed to be screened per fixed number of lung cancers detected,” Martin C. Tammemägi, PhD, of the department of community health sciences at Brock University in Ontario, Canada, and colleagues wrote.
The researchers modified the PLCO-based risk-prediction model to ensure applicability to NLST data as part of their effort to determine which was more efficient.
The comparison study incorporated 73,618 smokers in the PLCO study and 51,033 NLST participants with available epidemiologic data, as well as all histologically confirmed lung cancers that were diagnosed from study entry through 6 years of follow-up. Tammemägi and colleagues used Cox models to assess whether the reduction in mortality among 53,202 patients who underwent low-dose CT screening or chest X-ray in the NLST differed according to risk.
When compared with NLST criteria, the criteria from the PLCO-based risk-prediction model demonstrated improved sensitivity (83.0% vs. 71.1%, P<0.001) and positive predictive value (4.0% vs. 3.4%, P=0.01) for detecting lung cancers, without loss of specificity (62.9% and 62.7%, respectively; P=0.54). In addition, 41.3% fewer lung cancers were missed, according to researchers.
“Because the mortality reduction from CT screening effectiveness did not vary according to lung cancer risk, it appears that use of the [PLCO model] 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,” Tammemägi and colleagues wrote.
Perspective
Back to TopPeter Mazzone, MD, MPH, FCCP
The NLST showed that lung cancer mortality could be reduced by low-dose CT screening in a high-risk group. High risk was identified as an active or former smoker of at least 30 pack-years, who had smoked in the past 15 years and was aged 55 to 74 years. The current study developed a lung cancer risk calculator that was more sensitive at detecting those who would be diagnosed with lung cancer than the NLST criteria. The risk calculator was developed using data from individuals enrolled in screening trials who were 55 to 74 years old and had a smoking history. It is not clear how the calculator would perform on populations outside of those parameters. A threshold of risk for which screening would have more benefit than harm has not been developed. An accurate risk calculator could minimize the number of people who would need to be enrolled in a screening program to save a life from lung cancer, leading to less radiation exposure for those without lung cancer, and a more cost-effective program. Further study of this tool incorporated into screening trials seems warranted.
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