scholarly journals Comparing Benefits from Many Possible Computed Tomography Lung Cancer Screening Programs: Extrapolating from the National Lung Screening Trial Using Comparative Modeling

PLoS ONE ◽  
2014 ◽  
Vol 9 (6) ◽  
pp. e99978 ◽  
Author(s):  
Pamela M. McMahon ◽  
Rafael Meza ◽  
Sylvia K. Plevritis ◽  
William C. Black ◽  
C. Martin Tammemagi ◽  
...  
2013 ◽  
Vol 31 (8) ◽  
pp. 1002-1008 ◽  
Author(s):  
Denise R. Aberle ◽  
Fereidoun Abtin ◽  
Kathleen Brown

The National Lung Screening Trial (NLST) has provided compelling evidence of the efficacy of lung cancer screening using low-dose helical computed tomography (LDCT) to reduce lung cancer mortality. The NLST randomized 53,454 older current or former heavy smokers to receive LDCT or chest radiography (CXR) for three annual screens. Participants were observed for a median of 6.5 years for outcomes. Vital status was available in more than 95% of participants. LDCT was positive in 24.2% of screens, compared with 6.9% of CXRs; more than 95% of all positive LDCT screens were not associated with lung cancer. LDCT detected more than twice the number of early-stage lung cancers and resulted in a stage shift from advanced to early-stage disease. Complications of LDCT screening were minimal. Lung cancer–specific mortality was reduced by 20% relative to CXR; all-cause mortality was reduced by 6.7%. The major harms of LDCT are radiation exposure, high false-positive rates, and the potential for overdiagnosis. This review discusses the risks and benefits of LDCT screening as well as an approach to LDCT implementation that incorporates systematic screening practice with smoking cessation programs and offers opportunities for better determination of appropriate risk cohorts for screening and for better diagnostic prediction of lung cancer in the setting of screen-detected nodules. The challenges of implementation are considered for screening programs, for primary care clinicians, and across socioeconomic strata. Considerations for future research to complement imaging-based screening to reduce the burden of lung cancer are discussed.


2018 ◽  
Vol 26 (1) ◽  
pp. 50-56
Author(s):  
Christopher R Gilbert ◽  
Alexander S Carlson ◽  
Candice L Wilshire ◽  
Ralph W Aye ◽  
Alexander S Farivar ◽  
...  

Objective The National Lung Screening Trial demonstrated the benefits of lung cancer screening, but the potential high incidence of unnecessary invasive testing for ultimately benign radiologic findings causes concern. We aimed to review current biopsy patterns and outcomes in our community-based program, and retrospectively apply malignancy prediction models in a lung cancer screening population, to identify the potential impact these calculators could have on biopsy decisions. Methods Retrospective review of lung cancer-screening program participants from 2013 to 2016. Demographic, biopsy, and outcome data were collected. Malignancy risk calculators were retrospectively applied and results compared in patients with positive imaging findings. Results From 520 individuals enrolled in the screening program, pulmonary nodule(s) ≥6 mm were identified in 166, with biopsy in 30. Malignancy risk probabilities were significantly higher (Brock p < 0.00001; Mayo p < 0.00001) in those undergoing diagnostic sampling than those not undergoing sampling. However, there was no difference in the Brock ( p = 0.912) or Mayo ( p = 0.435) calculators when discriminating a final diagnosis of cancer from not cancer in those undergoing sampling. Conclusions In our screening program, 5.7% of individuals undergo invasive testing, comparable with the National Lung Screening Trial (6.1%). Both Brock and Mayo calculators perform well in indicating who may be at risk of malignancy, based on clinical and radiologic factors. However, in our invasive testing group, the Brock and Mayo calculators and Lung Cancer Screening Program clinical assessment all lacked clarity in distinguishing individuals who have a cancer from those with a benign abnormality.


2009 ◽  
Vol 37 (3) ◽  
pp. 268-279 ◽  
Author(s):  
Elyse R. Park ◽  
Jamie S. Ostroff ◽  
William Rakowski ◽  
Ilana F. Gareen ◽  
Michael A. Diefenbach ◽  
...  

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