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Editorials |5 November 2019

Using Risk Models to Make Lung Cancer Screening Decisions: Evidence-Based and Getting Better

Tanner J. Caverly, MD, MPH; Rafael Meza, PhD

Article, Author, and Disclosure Information
Author, Article, and Disclosure Information
This article was published at Annals.org on 22 October 2019.
  • VA Ann Arbor Healthcare System and University of Michigan, Ann Arbor, Michigan (T.J.C.)
    University of Michigan, Ann Arbor, Michigan (R.M.)

    Acknowledgment: The authors thank Rodney Hayward and Jeremy Sussman for reviewing the ideas in this manuscript.

    Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M19-2869.

    Corresponding Author: Tanner J. Caverly, MD, MPH, VA Center for Clinical Management Research and University of Michigan Medical School, 2800 Plymouth Road, Building 16, Room 331, Ann Arbor, MI 48109-2800; e-mail, tcaverly@med.umich.edu.

    Current Author Addresses: Dr. Caverly: VA Center for Clinical Management Research and University of Michigan Medical School, 2800 Plymouth Road, Building 16, Room 331, Ann Arbor, MI 48109-2800.

    Dr. Meza: Department of Epidemiology, School of Public Health, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109-2029.

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Two smokers are considering lung cancer screening with low-dose computed tomography (CT): Mr. Ryan, a 72-year-old man with several comorbidities, and Mrs. King, a 55-year-old woman who is otherwise healthy. Are these very different patients good screening candidates? How can we estimate their potential benefit from CT screening? How should we approach the question? The study by Cheung and colleagues provides one clear answer while adding a new wrinkle (1).
Deciding who should undergo screening involves a tradeoff between accuracy (how well does the strategy identify good screening candidates?) and complexity (how hard is it to implement the strategy in clinical practice?). Traditionally, guidelines have opted for simplicity, recommending screening according to age (such as for colorectal cancer); age and sex (such as for breast and prostate cancer); or, sometimes, fulfillment of study criteria (as in the recommendations for lung cancer screening). However, the introduction of easy-to-use computational technology has made it feasible to choose accuracy, using risk models to obtain more precise estimates of risk and benefit for patients. This will make screening more efficient, safe, and patient-centered.
Current guidelines for selecting CT screening candidates derive from the inclusion criteria used in the NLST (National Lung Screening Trial) (2, 3), which comprise cutoffs based on age (55 to 77 or 80 years) and smoking history (≥30 pack-years of smoking and <15 years since quitting). These recommendations follow the rules of the trial but will not necessarily lead to the best possible care for each...
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Caverly TJ, Meza R. Using Risk Models to Make Lung Cancer Screening Decisions: Evidence-Based and Getting Better. Ann Intern Med. 2019;171:669–670. [Epub ahead of print 22 October 2019]. doi: https://doi.org/10.7326/M19-2869

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Published: Ann Intern Med. 2019;171(9):669-670.

DOI: 10.7326/M19-2869

Published at www.annals.org on 22 October 2019

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