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. 2014 Dec 2;11(12):e1001764.
doi: 10.1371/journal.pmed.1001764. eCollection 2014 Dec.

Evaluation of the lung cancer risks at which to screen ever- and never-smokers: screening rules applied to the PLCO and NLST cohorts

Affiliations

Evaluation of the lung cancer risks at which to screen ever- and never-smokers: screening rules applied to the PLCO and NLST cohorts

Martin C Tammemägi et al. PLoS Med. .

Erratum in

Abstract

Background: Lung cancer risks at which individuals should be screened with computed tomography (CT) for lung cancer are undecided. This study's objectives are to identify a risk threshold for selecting individuals for screening, to compare its efficiency with the U.S. Preventive Services Task Force (USPSTF) criteria for identifying screenees, and to determine whether never-smokers should be screened. Lung cancer risks are compared between smokers aged 55-64 and ≥ 65-80 y.

Methods and findings: Applying the PLCO(m2012) model, a model based on 6-y lung cancer incidence, we identified the risk threshold above which National Lung Screening Trial (NLST, n = 53,452) CT arm lung cancer mortality rates were consistently lower than rates in the chest X-ray (CXR) arm. We evaluated the USPSTF and PLCO(m2012) risk criteria in intervention arm (CXR) smokers (n = 37,327) of the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO). The numbers of smokers selected for screening, and the sensitivities, specificities, and positive predictive values (PPVs) for identifying lung cancers were assessed. A modified model (PLCOall2014) evaluated risks in never-smokers. At PLCO(m2012) risk ≥ 0.0151, the 65th percentile of risk, the NLST CT arm mortality rates are consistently below the CXR arm's rates. The number needed to screen to prevent one lung cancer death in the 65th to 100th percentile risk group is 255 (95% CI 143 to 1,184), and in the 30th to <65th percentile risk group is 963 (95% CI 291 to -754); the number needed to screen could not be estimated in the <30th percentile risk group because of absence of lung cancer deaths. When applied to PLCO intervention arm smokers, compared to the USPSTF criteria, the PLCO(m2012) risk ≥ 0.0151 threshold selected 8.8% fewer individuals for screening (p<0.001) but identified 12.4% more lung cancers (sensitivity 80.1% [95% CI 76.8%-83.0%] versus 71.2% [95% CI 67.6%-74.6%], p<0.001), had fewer false-positives (specificity 66.2% [95% CI 65.7%-66.7%] versus 62.7% [95% CI 62.2%-63.1%], p<0.001), and had higher PPV (4.2% [95% CI 3.9%-4.6%] versus 3.4% [95% CI 3.1%-3.7%], p<0.001). In total, 26% of individuals selected for screening based on USPSTF criteria had risks below the threshold PLCO(m2012) risk ≥ 0.0151. Of PLCO former smokers with quit time >15 y, 8.5% had PLCO(m2012) risk ≥ 0.0151. None of 65,711 PLCO never-smokers had PLCO(m2012) risk ≥ 0.0151. Risks and lung cancers were significantly greater in PLCO smokers aged ≥ 65-80 y than in those aged 55-64 y. This study omitted cost-effectiveness analysis.

Conclusions: The USPSTF criteria for CT screening include some low-risk individuals and exclude some high-risk individuals. Use of the PLCO(m2012) risk ≥ 0.0151 criterion can improve screening efficiency. Currently, never-smokers should not be screened. Smokers aged ≥ 65-80 y are a high-risk group who may benefit from screening. Please see later in the article for the Editors' Summary.

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Conflict of interest statement

CB is a consultant for Medial Cancer Screening, Ltd., a diagnostic algorithm start-up company based in Tel Aviv working on improved methods of early detection of cancer using pre-existing information in the medical record.

Figures

Figure 1
Figure 1. Lung cancer mortality rates in NLST arms by PLCOm2012 model risk deciles.
PLCOm2012 model risk decile boundaries were established in PLCO control smokers. PLCOm2012 is the lung cancer risk prediction model described in .
Figure 2
Figure 2. Number of lung cancer cases and deaths in PLCO and NLST by PLCOm2012 percentiles of risk.
PLCO and NLST lung cancer cases and NLST lung cancer deaths were identified in 6 y of follow-up, and PLCO lung cancer deaths were identified in 11 y of follow-up. Calculations were based on PLCOm2012 deciles of risk, and the percentiles shown are the midpoints of each decile range. PLCOm2012 refers to the lung cancer risk prediction model described in .
Figure 3
Figure 3. NLST deaths from lung cancer and competing causes by trial arm and decile of PLCOm2012 risk.
CT is the LDCT screening arm; CXR is the CXR screening arm. PLCOm2012 refers to the lung cancer risk prediction model described in .
Figure 4
Figure 4. Distribution of PLCOm2012 risks in PLCO ever-smokers who are USPSTF-criteria-positive or are NLST participants.
The vertical line indicates the PLCOm2012 risk ≥0.0151 threshold. The graph is right-truncated. PLCOm2012 is the lung cancer risk prediction model described in .
Figure 5
Figure 5. PLCOm2012-estimated risks for high-risk individuals by smoking quit time in former smokers.
Estimates were prepared for white former smokers who are 68 y old, are high-school graduates, have a body mass index of 27 kg/m2, have no family history of lung cancer, have no personal history of cancer, started smoking at age 14 y, and smoked on average 30 cigarettes per day. As the quit time increases, smoking duration correspondingly decreases. The dotted horizontal line indicates the PLCOm2012 ≥0.0151 risk threshold. PLCOm2012 refers to the lung cancer risk prediction model described in .
Figure 6
Figure 6. Distribution of PLCOm2012 risk in PLCO intervention arm smokers with and without lung cancer diagnosed in 6 y of follow-up.
The risk threshold p = 0.0151 is indicated by the vertical line. The graph is right-truncated. PLCOm2012 is the lung cancer risk prediction model described in .
Figure 7
Figure 7. Distribution of PLCOm2012 risk and natural log-transformed risk in PLCO participants stratified by age dichotomized at 65 y.
The PLCOm2012 risk ≥0.0151 threshold is marked by the dotted vertical line. The upper graph is right-truncated. PLCOm2012 is the lung cancer risk prediction model described in .

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