Home Thinking Beyond the Treatment Effect in Screening for Lung Cancer
Post
Cancel

Thinking Beyond the Treatment Effect in Screening for Lung Cancer

In the optimism surrounding screening for lung cancer, the downsides of screening should not be ignored. The article examines the dark side of screening, which is not an unmitigated good.

Cautiously optimistic for low-dose computed tomography (LDCT) screening for lung cancer, an editorial warned “too often, presumed solutions have prematurely become standard medical care before the appropriate studies have been completed. We strongly recommend that well-designed studies be conducted, completed, analyzed, and validated before a mass screening program is implemented.” The National Lung Screening Trial (NLST), which randomized smokers to annual LDCT or chest radiography for 3 years, fulfilled these requirements .

LDCT reduces lung cancer deaths by 0.4%. Screening 320 smokers for 3 years saves one smoker from death from lung cancer. Approval of LDCT by the Centers of Medicare and Medicaid (CMS) is a triumph of evidence-based medicine . Nevertheless, radiologists must think beyond the treatment effect.

Overdiagnosis is one of the more difficult harms of screening to conceptualize. We overdiagnose when we find disease in which treatment does not improve outcomes, harms exceed benefits, and the disease is clinically silent. Screening seeks cancer in the asymptomatic, a reservoir in which overdiagnosis is inevitable. Overdiagnosis is nebulous because it is difficult to quantify and predict. However, just because an entity cannot be precisely quantified does not mean the entity does not exist.

Cancer is biologically heterogeneous, and its spectrum includes the indolent and the aggressive. Probabilistically, screening more likely picks up a slower than a faster growing tumor. To understand this, consider an observer periodically watching cars on a freeway where the cars are infrequent, and there are slow and fast cars. The observer will more likely see a slower car. The proportion of cars detected by the observer that are slow cars depends on the mix of slow and fast cars, the difference in speed, and the frequency with which the observer watches cars.

Screening is less likely to detect faster growing, more aggressive, cancers. This is length time bias . It is a bias which humbles us because it means that screening is least effective against the most consequential cancers. Length time bias may be mitigated by greater vigilance, but this comes at the expense of radiation, costs, and false positives.

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

References

  • 1. Patz E.F., Goodman P.C., Bepler G.: Screening for lung cancer. NEJM 2000; 343: pp. 1627-1633.

  • 2. National Lung Screening Trial Research Team: Reduced lung-cancer mortality with low-dose computed tomography. NEJM 2011; 365: pp. 395-409.

  • 3. http://www.cms.gov/medicare-coverage-database/details/nca-decision-memo.aspx?NCAId=274 (accessed May 30, 2015)

  • 4. Hulka B.S.: Cancer screening: degrees of proof and practical application. Cancer 1988; 62: pp. 1776-1780.

  • 5. Fontana R.S., Sanderson D.R., Taylor W.F., et. al.: Early lung cancer detection: results of the initial (prevalence) radiologic and cytologic screening in the Mayo Clinic study. Am Rev Respir Dis 1984; 130: pp. 561-565.

  • 6. Patz E.F., Pinsky P., Gatsonis C., et. al.: Overdiagnosis in low-dose computed screening for lung cancer. JAMA Intern Med 2014; 174: pp. 269-274.

  • 7. Travis W.D., Brambilla E., Noguchi M., et. al.: International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society international multidisciplinary classification of lung adenocarcinoma. J Thorac Oncol 2011; 6: pp. 244-285.

  • 8. Laibson D.: Golden eggs and hyperbolic discounting. The Quarterly Journal of Economics 1997; 112: pp. 443-478.

  • 9. Pinsky P.F., Gierada D.S., Black W., et. al.: Performance of Lung-RADS in the National Lung Screening Trial: a retrospective assessment. Ann Intern Med 2015; 162: pp. 485-491.

  • 10. http://www.acr.org/Quality-Safety/Resources/LungRADS (accessed May 30, 2015)

This post is licensed under CC BY 4.0 by the author.