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Selecting lung cancer screenees using risk prediction models-where do we go from here
1Department of Health Sciences, Brock University, Walker Complex - Academic South, St. Catharines, Ontario, Canada.
Translational Lung Cancer Research
|July 28, 2018
Summary
Lung cancer screening using low-dose computed tomography (LDCT) can be improved by using risk models instead of current criteria. This approach identifies more high-risk individuals for lung cancer screening, enhancing effectiveness and cost-efficiency.
Area of Science:
- Pulmonology
- Oncology
- Radiology
Background:
- National Lung Screening Trial (NLST) demonstrated low-dose computed tomography (LDCT) reduces lung cancer mortality by 20% in high-risk individuals.
- Current lung cancer screening eligibility criteria (e.g., USPSTF, CMS) are based on age, smoking history, and pack-years.
- Model-estimated risk selection for lung cancer screening shows promise over traditional criteria.
Purpose of the Study:
- To evaluate the superiority of model-estimated risk over NLST-like criteria for lung cancer screening selection.
- To assess the impact of excluding low-risk individuals from screening on cohort characteristics.
- To consider the role of race and ethnicity in model-based lung cancer screening eligibility.
Main Methods:
- Analysis of retrospective data and ongoing prospective trials (ILST, Lung Cancer Screening Pilot for People at High Risk).
- Comparison of screening selection using model-based risk (e.g., PLCOm2012) versus NLST-like criteria.
- Evaluation of lung cancer incidence, sensitivity, positive predictive value (PPV), and cost-effectiveness.
Main Results:
- Model-based risk selection demonstrates higher sensitivity, PPV, more averted deaths, and better cost-effectiveness than NLST-like criteria.
- Excluding 25.6% of NLST-eligible smokers at low risk results in a screening group with similar age, comorbidity, and competing mortality risks to the PLCOm2012+ve group.
- Interim results from ILST and the Cancer Care Ontario pilot support the risk-based approach to lung cancer screening.
Conclusions:
- Risk-based selection models are superior to current criteria for identifying individuals who benefit from lung cancer screening.
- Excluding low-risk individuals from screening improves the characteristics of the screened population.
- Future lung cancer screening programs should consider incorporating risk models, potentially adjusting for racial and ethnic disparities.
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