Benchmarking lung cancer screening programmes with adaptive screening frequency against the optimal screening schedules derived from the ENGAGE framework: a comparative microsimulation study
- PMID: 39764179
- PMCID: PMC11701438
- DOI: 10.1016/j.eclinm.2024.102743
Benchmarking lung cancer screening programmes with adaptive screening frequency against the optimal screening schedules derived from the ENGAGE framework: a comparative microsimulation study
Abstract
Background: Lung cancer screening recommendations employ annual frequency for eligible individuals, despite evidence that it may not be universally optimal. The impact of imposing a structure on the screening frequency remains unknown. The ENGAGE framework, a validated framework that offers fully dynamic, analytically optimal, personalised lung cancer screening recommendations, could be used to assess the impact of screening structure on the effectiveness and efficiency of lung cancer screening.
Methods: In this comparative microsimulation study, we benchmarked alternative clinically relevant structured lung cancer screening programmes employing a fixed (annual or biennial) or adaptive (start with annual/biennial screening and then switch to biennial/annual at ages 60- or 65-years) screening frequency, against the ENGAGE framework. Individuals were eligible for screening according to the 2021 US Preventive Services Task Force recommendation on lung cancer screening. We assessed programmes' efficiency based on the number of screenings per death avoided (LDCT/DA) and the number of screenings per ever-screened individual (LDCT/ESI), and programmes' effectiveness using quality-adjusted life years (QALY) gained from screening, lung cancer-specific mortality reduction (MR), and number of screen-detected lung cancer cases. We used validated natural history, smoking history generator, and risk prediction models to inform our analysis. Sensitivity analysis of key inputs was conducted.
Findings: ENGAGE was the best performing strategy. Among the structured policies, adaptive biennial-to-annual at age 65 was the best strategy requiring 24% less LDCT/DA and 60% less LDCT/ESI compared to TF2021, but yielded 105 more deaths per 100,000 screen-eligible individuals (10.2% vs. 11.8% MR for TF2021, p = 0.28). Fixed annual screening was the most effective strategy but the least efficient and was ranked as the fifth best strategy. All strategies yielded similar QALYs gained. Adherence levels did not affect the rankings.
Interpretation: Adaptive lung cancer screening strategies that start with biennial and switch to annual screening at a prespecified age perform well and warrant further consideration, especially in settings with limited availability of CT scanners and radiologists.
Funding: National Cancer Institute.
Keywords: Adaptive screening; ENGAGE; Early detection; Low-dose CT; Lung cancer screening.
© 2024 The Author(s).
Conflict of interest statement
Mehdi Hemmati has no conflict of interest to report. Sayaka Ishizawa has no conflict of interest to report. Edwin Ostrin has benefitted from “Early Detection Research Network Clinical Validation Center (NCI)” grant. Dr. Ostrin has presented on lung cancer screening in Astra Zeneca (April 2021) and Texas Association of Family Practitioners (Nov 23). He has received support for attending the 2020 Gene Systems (February, July 2023) ad GRAIL (December 2023). Dr. Ostrin has a patent, “intellectual property on a 4-protein blood biomarker panel for lung cancer early detection.” He has served in GRAIL Scientific Advisory Board (Dec 2023). Dr. Ostrin is also involved in continuing negotiation with 2020 Gene Systems (Gaithersburg, MD) for bringing blood biomarker panel for lung cancer to marker. Samir M. Hanash has received support from NCI Lung CVC. Mara Antonoff received payment or honoraria for lectures, presentations, speakers, bureaus, manuscript writing, or educational events held by Merck, Bristol Myers Squibb (BMS), Ethicon, and AstraZeneca. Andrew J. Schaefer has no conflict of interest to report. Martin C. Tammemägi has no conflict of interest to report. Iakovos Toumazis has received support from NIH/NCI (R37CA271187, U01CA253858, F32CA220961, and U01CA199284) and has served as an expert advisor to the American Cancer Society (ACS) Guideline Development Group for the update of the ACS lung cancer screening guideline.
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