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Improving the Efficiency of Computed Tomography Lung Cancer Screening.

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Restricting screening to highest-risk smokers would retain most of the benefit.
In the randomized National Lung Screening Trial (NLST), three annual screenings with low-dose computed tomography (CT) lowered lung cancer–related mortality among current or former (within 15 years) smokers (age range, 55–74) with smoking histories of ≥30 pack-years (NEJM JW Gen Med Jul 14 2011). But the relatively small absolute benefit (3 fewer deaths per 1000 screened during average follow-up of 6.5 years) and the high rate of false-positive CT findings raise this question: Can we target screening to a subgroup of smokers most likely to benefit?

To address this question, researchers used data from the NLST control group to develop a risk-prediction model for lung cancer–related death; the model incorporated age, sex, race, family history, details of smoking history (i.e., pack-years, time since smoking cessation), and known pulmonary disease. Next, the researchers used the model to divide NLST participants into quintiles of 5-year risk for lung cancer–related death, which ranged from <0.5% in the first quintile to >2.0% in the fifth quintile.

The number of lung cancer deaths prevented by CT screening ranged from 1 per 5300 (in the lowest-risk quintile) to 33 per 5300 (in the highest-risk quintile). Thus, the number needed to screen to prevent 1 death ranged from 5300 in the lowest-risk quintile to 161 in the highest-risk quintile. Rates of false-positive scans were high in all quintiles (between 30% and 40%).

COMMENT

This is an important analysis. It shows that, by refining the eligibility criteria for CT screening, we could retain nearly all the benefits while lowering the number of people screened, costs, and burdens of false-positive scans.

Source: NEJM

Selection criteria for lung-cancer screening.

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The National Lung Screening Trial (NLST) used risk factors for lung cancer (e.g., >/=30 pack-years of smoking and <15 years since quitting) as selection criteria for lung-cancer screening. Use of an accurate model that incorporates additional risk factors to select persons for screening may identify more persons who have lung cancer or in whom lung cancer will develop.
METHODS: We modified the 2011 lung-cancer risk-prediction model from our Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial to ensure applicability to NLST data; risk was the probability of a diagnosis of lung cancer during the 6-year study period. We developed and validated the model (PLCO(M2012)) with data from the 80,375 persons in the PLCO control and intervention groups who had ever smoked. Discrimination (area under the receiver-operating-characteristic curve [AUC]) and calibration were assessed. In the validation data set, 14,144 of 37,332 persons (37.9%) met NLST criteria. For comparison, 14,144 highest-risk persons were considered positive (eligible for screening) according to PLCO(M2012) criteria. We compared the accuracy of PLCO(M2012) criteria with NLST criteria to detect lung cancer. Cox models were used to evaluate whether the reduction in mortality among 53,202 persons undergoing low-dose computed tomographic screening in the NLST differed according to risk.
RESULTS: The AUC was 0.803 in the development data set and 0.797 in the validation data set. As compared with NLST criteria, PLCO(M2012) criteria had improved sensitivity (83.0% vs. 71.1%, P<0.001) and positive predictive value (4.0% vs. 3.4%, P=0.01), without loss of specificity (62.9% and. 62.7%, respectively; P=0.54); 41.3% fewer lung cancers were missed. The NLST screening effect did not vary according to PLCO(M2012) risk (P=0.61 for interaction).
CONCLUSIONS: The use of the PLCO(M2012) model was more sensitive than the NLST criteria for lung-cancer detection.

Source:NEJM