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Correspondence |

Targeted CT Image Screening and Its Effect on Lung Cancer Detection RateTargeted CT: Effect on Lung Cancer Detection Rate FREE TO VIEW

Robert P. Young, MD, PhD; Raewyn J. Hopkins, BN, MPH
Author and Funding Information

From the School of Biological Sciences and Faculty of Medical and Health Sciences, The University of Auckland.

Correspondence to: Robert P. Young, MD, PhD, Respiratory Genetics Group, PO Box 26161, Epsom 1344, Auckland, New Zealand; e-mail: roberty@adhb.govt.nz


Financial/nonfinancial disclosures: The authors have reported to CHEST the following conflicts of interest: Dr Young, and the funding of his research, has been supported by grants from The University of Auckland, Health Research Council of New Zealand, and Synergenz BioSciences Ltd. Synergenz BioSciences Ltd holds patents for gene-based risk testing for lung cancer susceptibility; Dr Young is an unpaid consultant to Synergenz Bioscience Ltd. Ms Hopkins has reported that no potential conflicts of interest exist with any companies/organizations whose products or services may be discussed in this article.

Reproduction of this article is prohibited without written permission from the American College of Chest Physicians. See online for more details.


Chest. 2013;144(4):1419-1420. doi:10.1378/chest.13-1321
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Published online
To the Editor:

Although we broadly endorse the American College of Chest Physicians lung cancer screening guidelines, outlined in the recent article by Detterbeck et al1 in CHEST (May 2013), we disagree that the impact of differing eligibility criteria are unknown.2,3

The assumption underlying the National Lung Screening Trial (NLST) eligibility criteria is that they identify those at greatest risk and the most to gain from screening. The former assumption can be easily tested by comparing the number of lung cancers detected per year per person screened (lung cancer detection rate [LCDR]) across lung cancer screening studies with differing eligibility criteria.2 Although this metric does not assess lives saved, it does reflect the precision of risk prediction.2,3 Our analysis suggests that there is surprising consistency in the LCDR across screening studies using CT imaging, despite differing age and smoking criteria (Table 1).4 Primarily, the higher age and smoking history criteria of the NLST did not translate into significantly more cancers detected (reflected in the LCDR) than other screening programs that screened people with wider age and pack-year histories. LCDR is an important metric as its inverse estimates the number needed to screen in a year to detect one lung cancer (NND), which is about 110 to 120 people in the case of NLST. This has important implications with respect to optimizing the cost-to-benefit and benefit-to-harm ratios, which remain major concerns impeding the wider adoption of lung cancer screening.1-3

Table Graphic Jump Location
Table 1 —LCDR in the CT Imaging Arms of Recent Lung Cancer Screening Studies Compared With the NLST

Lung cancer detection rate (lung cancers detected per year per person screened) in the CT imaging arms of recent lung cancer screening studies compared with the NLST. COSMOS = Continuous Observation of Smoking Subjects; CTE = CT imaging-emphysema; DLST = Danish Lung Cancer Screening Trial; FHx = family history of lung cancer; GOLD = Global Initiative for Chronic Obstructive Lung Disease; Hx COPD = self-reported COPD; LCDR = lung cancer detection rate; NELSON = The Dutch-Belgian Randomized Lung Cancer Screening Trial; NLST = National Lung Screening Trial; NND = the number of people needed to be screened for 1 y to detect one lung cancer; PLCO = Prostate, Lung, Colorectal, and Ovarian; PLuSS = The Pittsburgh Lung Screening Study.

a 

Data for the NLST were for the first 3 y when yearly CT image screening was performed.

b 

Normal lungs means no airflow limitation on spirometry and no emphysema on CT imaging.

c 

The model derived from the Prostate Lung, Colorectal and Ovarian study.

Recently, we showed that when genetic and COPD-based risk variables are combined with age and smoking histories (eg, multivariate risk model),3 LCDR can be doubled (Table 1).2,5 This translates to a halving of the number of people needed to screen to detect one lung cancer and a reduction in costs associated with lung cancer detection. The NLST-validated Prostate, Lung, Colorectal, and Ovarian-based model includes variables that encompass genetic risk (family history of lung cancer) and the presence or risk of COPD (self-reported COPD and low BMI). By improving the efficiency of screening (lowering the NND), through multivariate models with greater predictive utility than NLST-based risk prediction, we anticipate the absolute number of false-positives detected and investigated (benign and indolent “overdiagnosed” nodules) will be reduced.2-6

We conclude that while the NLST successfully showed that CT image screening reduces lung cancer deaths, further refinement of lung cancer screening eligibility criteria might better target the highest-risk smokers. This could significantly improve the LCDR and lower the costs to detect lung cancer. Further analysis will be needed to determine the benefit with respect to lives saved.

References

Detterbeck FC, Mazzone PJ, Naidich DP, Bach PB. Screening for lung cancer: diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest. 2013;143(5_suppl):e78S-e92S.
 
Young RP, Hopkins RJ. Is 20% of a loaf enough? Cancer. 2013;119(15):2815. [CrossRef] [PubMed]
 
Tammemägi MC, Katki HA, Hocking WG, et al. Selection criteria for lung-cancer screening. N Engl J Med. 2013;368(8):728-736. [CrossRef] [PubMed]
 
de-Torres JP, Casanova C, Marin JM, et al. Exploring the impact of screening with low-dose CT on lung cancer mortality in mild to moderate COPD patients: a pilot study. Respir Med. 2013;107(5):702-707. [CrossRef] [PubMed]
 
Young RP, Hopkins RJ. Diagnosing COPD and targeted lung cancer screening. Eur Respir J. 2012;40(4):1063-1064. [CrossRef] [PubMed]
 
Young RP, Hopkins RJ. Estimating overdiagnosis of lung cancer. Ann Intern Med. 2013;158(8):635-636. [CrossRef] [PubMed]
 

Figures

Tables

Table Graphic Jump Location
Table 1 —LCDR in the CT Imaging Arms of Recent Lung Cancer Screening Studies Compared With the NLST

Lung cancer detection rate (lung cancers detected per year per person screened) in the CT imaging arms of recent lung cancer screening studies compared with the NLST. COSMOS = Continuous Observation of Smoking Subjects; CTE = CT imaging-emphysema; DLST = Danish Lung Cancer Screening Trial; FHx = family history of lung cancer; GOLD = Global Initiative for Chronic Obstructive Lung Disease; Hx COPD = self-reported COPD; LCDR = lung cancer detection rate; NELSON = The Dutch-Belgian Randomized Lung Cancer Screening Trial; NLST = National Lung Screening Trial; NND = the number of people needed to be screened for 1 y to detect one lung cancer; PLCO = Prostate, Lung, Colorectal, and Ovarian; PLuSS = The Pittsburgh Lung Screening Study.

a 

Data for the NLST were for the first 3 y when yearly CT image screening was performed.

b 

Normal lungs means no airflow limitation on spirometry and no emphysema on CT imaging.

c 

The model derived from the Prostate Lung, Colorectal and Ovarian study.

References

Detterbeck FC, Mazzone PJ, Naidich DP, Bach PB. Screening for lung cancer: diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest. 2013;143(5_suppl):e78S-e92S.
 
Young RP, Hopkins RJ. Is 20% of a loaf enough? Cancer. 2013;119(15):2815. [CrossRef] [PubMed]
 
Tammemägi MC, Katki HA, Hocking WG, et al. Selection criteria for lung-cancer screening. N Engl J Med. 2013;368(8):728-736. [CrossRef] [PubMed]
 
de-Torres JP, Casanova C, Marin JM, et al. Exploring the impact of screening with low-dose CT on lung cancer mortality in mild to moderate COPD patients: a pilot study. Respir Med. 2013;107(5):702-707. [CrossRef] [PubMed]
 
Young RP, Hopkins RJ. Diagnosing COPD and targeted lung cancer screening. Eur Respir J. 2012;40(4):1063-1064. [CrossRef] [PubMed]
 
Young RP, Hopkins RJ. Estimating overdiagnosis of lung cancer. Ann Intern Med. 2013;158(8):635-636. [CrossRef] [PubMed]
 
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