Lung Cancer Screening Model Description

Evidence base for risk assessments

Bottom Line Results

All patients who are eligible for lung cancer screening with low-dose computed tomography should get shared decision making. However, results from a modeling study completed by researchers at the Ann Arbor VA and University of Michigan found that screening is more preference sensitive for some eligible patients than it is for others. This is because the mortality benefit with LDCT screening is much larger for some eligible patients who are at higher risk for developing lung cancer.

Preference sensitive decisions are:

  • Decisions that do not have one right answer
  • Depend on the patient's preferences and values
  • Take into account the patient's feelings about the pros and cons of each option

We found that two criteria can place patients in the preference-sensitive category. The criteria are:

  • Life expectancy less than 10.5 years, OR
  • Estimated 6-year risk of developing lung cancer less than 1.8%

In other words, preferences are most important in determining the best decision for patients who have limited life-expectancy (< 10 years) or who have lower baseline risk for developing lung cancer (6-year risk < 1.8%).

For persons with a longer life expectancy (> 10 years) AND at higher baseline lung cancer risk (6-year risk >1.8%), our results suggest that the net benefit of lung cancer screening is less sensitive to differences in patient preferences. In other words, screening can be considered high benefit for these patients. Screening can generally be encouraged for high benefit patients based on the large expected mortality benefit relative to the downsides of screening.

The line below depicts these 2 categories of benefit graphically: 1) preference sensitive and 2) high benefit.

Lowest risk among eligible patients
Highest risk among eligible patients

Screening is preference sensitive*

Screening is high benefit

* Best option depends on patient preferences

Methods Overview

We used a microsimulation model to estimate the impact of LDCT screening on a US representative population of persons meeting current eligibility criteria (i.e., age 55-80, > 30 pack-years, current smoker or quit < 15 years ago).

Model probabilities were based on previously published work (1–10) and lung cancer histology, stage, and survival data from the Surveillance, Epidemiology and End-Results (SEER) cancer registry.

Preferences about the benefits and harms of screening were quantified using a single measure (utility) to aid comparisons between disparate types of outcomes (e.g., reduction in mortality vs. false-positive finding). We used measures of utility and disutility reported in previously published research.(11,12)

We categorized LDCT screening as a preference sensitive close call for a patient when moderate differences in preferences had a large impact on presence of net benefit. We categorized LDCT screening as high benefit for a patient when moderate differences in preferences had negligible impact on the presence of net benefit.

A complete description of the model and detailed study methods will be available at a later date.

Graphically representing the evidence

To graphically represent the preference sensitive and high benefit groups in the most easily understood way, we simplified the visuals we used. The most important design decision we made was creating a clear cutoff between the two groups. However, the reality is slightly  more complicated. Like any statistically driven definition of categories, there’s a gray-zone between groups. If the design of the graphical representation were to reflect this gray-zone, the yellow and green would fade into each other.

To provide clear guidance to busy PCPs, we represented the threshold between these two groups as a defined change in color at the point where the gray-zone ends and there’s more certainty that a patient is high benefit. As a result, every patient who might have fallen in the gray-zone is represented as a preference-sensitive patient in the yellow-zone. In other words, we chose to err on the side of including more patients in the preference-sensitive category.

Lowest risk among eligible patients
Highest risk among eligible patients

Screening is preference sensitive*

Screening is high benefit

* Best option depends on patient preferences

Other models for assessing lung cancer risk
TBD
References
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  2. Bach PB, Elkin EB, Pastorino U, Kattan MW, Mushlin AI, Begg CB, et al. Benchmarking lung cancer mortality rates in current and former smokers*. CHEST J. 2004 Dec 1;126(6):1742–9.
  3. Bach PB, Kattan MW, Thornquist MD, Kris MG, Tate RC, Barnett MJ, et al. Variations in lung cancer risk among smokers. J Natl Cancer Inst. 2003;95(6):470–478.
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  8. Haaf K ten, Rosmalen J van, Koning HJ de. Lung cancer detectability by test, histology, stage and gender: estimates from the NLST and the PLCO trials. Cancer Epidemiol Biomarkers Prev. 2014 Oct 13;cebp.0745.2014.
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  10. Pinsky PF, Gierada DS, Black W, Munden R, Nath H, Aberle D, et al. Performance of Lung-RADS in the National Lung Screening TrialA Retrospective AssessmentPerformance of Lung-RADS in the NLST. Ann Intern Med. 2015 Apr 7;162(7):485–91.
  11. Black WC, Gareen IF, Soneji SS, Sicks JD, Keeler EB, Aberle DR, et al. Cost-Effectiveness of CT Screening in the National Lung Screening Trial. N Engl J Med. 2014 Nov 5;371(19):1793–802.
  12. Mahadevia PJ, Fleisher LA, Frick KD, Eng J, Goodman SN, Powe NR. Lung cancer screening with helical computed tomography in older adult smokers: A decision and cost-effectiveness analysis. JAMA. 2003 Jan 15;289(3):313–22.