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When the Average Applies to No One: Personalized Decision Making About Potential Benefits of Lung Cancer Screening

Peter B. Bach, MD, MAPP; and Michael K. Gould, MD, MS
[+] Article, Author, and Disclosure Information

This article was published at www.annals.org on 14 August 2012.

From Memorial Sloan-Kettering Cancer Center, New York, New York, and Kaiser Permanente Southern California, Pasadena, California.

Acknowledgment: The authors thank Geoffrey Schnorr, BS (Memorial Sloan-Kettering Cancer Center), who provided administrative and editorial assistance.

Potential Conflicts of Interest: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M12-0406.

Requests for Single Reprints: Peter B. Bach, MD, MAPP, 1275 York Avenue, New York, NY 10065.

Current Author Addresses: Dr. Bach: Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY 10065.

Dr. Gould: Kaiser Permanente Southern California, 100 South Los Robles Avenue, Pasadena, CA 91101.

Author Contributions: Conception and design: P.B. Bach, M.K. Gould.

Analysis and interpretation of the data: P.B. Bach, M.K. Gould.

Drafting of the article: P.B. Bach.

Critical revision of the article for important intellectual content: P.B. Bach, M.K. Gould.

Final approval of the article: P.B. Bach, M.K. Gould.

Statistical expertise: P.B. Bach.

Administrative, technical, or logistic support: P.B. Bach.

Collection and assembly of data: P.B. Bach.

Ann Intern Med. 2012;157(8):571-573. doi:10.7326/0003-4819-157-8-201210160-00524
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This commentary discusses computed tomography screening of patients at risk for lung cancer, examining the magnitude of benefit for prototypical persons who may be offered screening. It concludes that the underlying chance of benefit from this screening should be taken into account when counseling patients who are considering computed tomography to screen for lung cancer.

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Predictive Accuracy of the Liverpool Lung Project (LLP) Risk Model for Stratifying Patients for Computed Tomography Screening for Lung Cancer
Posted on December 20, 2012
Robert P Young, MD, Raewyn J Hopkins, MD, David E Midthun, MD
Drs. Young and Hopkins -Schools of Biological Science and Faculty of Medical and Health Sciences, Univ of Auckland, New Zealand. Dr. Midthun - Mayo Clinic, Rochester, Minnesota, USA.
Conflict of Interest: Financial/nonfinancial disclosures: RPY, 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. DEM, has received payment for preparation of chapters on lung cancer screening for the American College of Physicians and royalties for a chapter on lung cancer in Up-to-Date. Financial/nonfinancial disclosures: RPY, 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. DEM, has received payment for preparation of chapters on lung cancer screening for the American College of Physicians and royalties for a chapter on lung cancer in Up-to-Date.


We agree with Raji et al.(1), that the selection of current and former smokers for computed tomographic (CT) screening for lung cancer should target those at greatest risk “to maximize the benefit-harm ratio” (2). While we concur that a multivariate approach to risk assessment is the best way to achieve this (3), we question whether the validated LLP model maximizes this benefit-harm ratio as suggested. Although the LLP multivariate model performs better than lung cancer risk models using age and smoking history alone (1), it is less clear that this superior performance translates into improved CT screening outcomes. In a recent opinion piece by Bach and Gould (4), it was strongly argued that screening should be limited to those at greatest risk so that the greatest number of deaths from lung cancer can be averted per person screened. Using the results of the National Lung Screening Trial (NLST), Bach et al. showed that the absolute number of deaths averted by screening is maximized when lung cancer detection rate (or death rate) is maximized. The study by Raji and colleagues (1) does not demonstrate a superior detection rate using the LLP model which the authors themselves have estimated to be only 1.0-1.5 fold that from using the NLST criteria (5). If the LLP model does not substantially increase the detection rate of lung cancer (1), it is difficult to see how it will maximize the benefit-harm ratio (4). Given the LLP model uses more variables than the Bach model (also externally validated (4)), and achieves a higher area-under-the-curve performance characteristic (AUC), why then might the detection rate be no different to the NLST criteria that uses age and smoking history alone? It may be because the arbitrary cut off of 5% used for selection based on the LLP model is too low. Only validation in a CT screening study will clarify this issue. We have recently shown, using the Pittsburgh CT Screening Study, that when eligible smokers were stratified according to the presence of COPD, the lung cancer detection rate was 5 fold greater than in those without COPD (2,3). It is therefore possible that the absence of COPD in the LLP model may reduce its utility in distinguishing those most at risk of lung cancer among older heavy smokers. The role of “COPD-related” risk factors in improving lung cancer detection rate is currently under investigation in the NLST (2).


1. Raji OY, Duffy SW, Agbaje OF, Baker SG, Christianni DC, et al. Predictive accuracy of the Liverpool Lung Project risk model for stratifying patients for computed tomography screening for lung cancer: A case-control and cohort validation study. Ann Int Med 2012; 157: 242-250.

2. Young RP, Hopkins RJ.CT screening for lung cancer. Thorax 2012; 67: 650-651.

3. Young RP, Hopkins RJ. Diagnosing COPD and targeted lung cancer screening. Eur Respir J 2012; doi:10.1183/09031936.0070012.

4. Bach PB, Gould MK. When the average applies to no one: personalized decision making about potential benefits of lung cancer screening. Ann Int Med 2012, August 14.

5. Baldwin DR, Duffy SW, Wald NJ, Page R, Hansell DM, Field JK, et al. UK Lung Screen (UKLS) nodule management protocol: modeling of a single screen randomized control trial of low-dose CT screening for lung cancer. Thorax 2011; 66: 308-313.

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