Evidence map›Paper›PMID 39764179›Full record

ArticleEClinicalMedicine2024

Benchmarking lung cancer screening programmes with adaptive screening frequency against the optimal screening schedules derived from the ENGAGE framework: a comparative microsimulation study.

Mehdi Hemmati, Sayaka Ishizawa, Rafael Meza, Edwin Ostrin, Samir M Hanash, Mara Antonoff, Andrew J Schaefer, Martin C Tammemägi, Iakovos Toumazis

Abstract read
In one paragraph

Article in EClinicalMedicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Mehdi HemmatiDivision of Cancer Prevention and Population Sciences, Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Sayaka IshizawaDivision of Cancer Prevention and Population Sciences, Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Rafael MezaBC Cancer Research Institute, Vancouver, BC, Canada.
Edwin OstrinDivision of Internal Medicine, Department of General Internal Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Samir M HanashDivision of Cancer Prevention and Population Sciences, Department of Clinical Center Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Mara AntonoffDivision of Surgery, Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Andrew J SchaeferDepartment of Computational Applied Mathematical and Operations Research, Rice University, Houston, TX, USA.
Martin C TammemägiDepartment of Community Health Sciences, Brock University, St. Catharines, ON, Canada.
Iakovos ToumazisDivision of Cancer Prevention and Population Sciences, Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Funding

Comparative Modeling of Lung Cancer Prevention, Early Detection and Treatment InterventionsU01CA253858 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DE KONING, HARRY J, HOLFORD, THEODORE R · 2020 to 2025
$8.4M
Optimizing Personalized Screening and Diagnostic Decisions for Lung Cancer Based on Dynamic Risk Assessment and Life ExpectancyR37CA271187 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Iakovos Toumazis · 2022 to 2026
$2.8M
NCI NIH HHS R37 CA271187NCI NIH HHS U01 CA253858
6 · The paper itself

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.

Indexed as

Adaptive screeningEarly detectionENGAGELow-dose CTLung cancer screening

Identifiers

PMID39764179
PMCPMC11701438

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.