Evidence map›Paper›PMID 36745885›Full record

ArticleAnnals of internal medicine2023

Risk Model-Based Lung Cancer Screening : A Cost-Effectiveness Analysis.

Iakovos Toumazis, Pianpian Cao, Koen de Nijs, Mehrad Bastani, Vidit Munshi, Mehdi Hemmati, Kevin Ten Haaf, Jihyoun Jeon, Martin Tammemägi, G Scott Gazelle and 6 more

Abstract read
In one paragraph

Article in Annals of internal medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 7 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

46 citing papers in PubMed, 7 syntheses or guidelines pooled it.

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  16. Improving ovarian cancer risk assessment using a machine learning model developed on data from the Prostate Lung Colorectal Ovarian Cancer Screening Trial.International journal of gynecological cancer : official journal of the International Gynecological Cancer Society · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

16 authors.

Iakovos ToumazisDepartment of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, Texas (I.T., M.H.).ORCID 0000-0002-3462-2137
Pianpian CaoDepartment of Epidemiology, University of Michigan, Ann Arbor, Michigan (P.C., J.J.).ORCID 0000-0001-8886-9672
Koen de NijsErasmus MC-University Medical Center, Rotterdam, the Netherlands (K. de N., K. ten H., H.J. de K.).ORCID 0000-0003-1451-0557
Mehrad BastaniFeinstein Institutes for Medical Research, Northwell Health, Manhasset, New York (M.B.).
Vidit MunshiDepartment of Radiology, Massachusetts General Hospital, Boston, Massachusetts (V.M., G.S.G.).
Mehdi HemmatiDepartment of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, Texas (I.T., M.H.).ORCID 0000-0002-0560-3893
Kevin Ten HaafErasmus MC-University Medical Center, Rotterdam, the Netherlands (K. de N., K. ten H., H.J. de K.).ORCID 0000-0001-5006-6938
Jihyoun JeonDepartment of Epidemiology, University of Michigan, Ann Arbor, Michigan (P.C., J.J.).ORCID 0000-0001-7003-3412
Martin TammemägiDepartment of Health Sciences, Brock University, St. Catharines, Ontario, Canada (M.T.).ORCID 0000-0002-4989-5058
G Scott GazelleDepartment of Radiology, Massachusetts General Hospital, Boston, Massachusetts (V.M., G.S.G.).
Eric J FeuerDivision of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland (E.J.F.).ORCID 0000-0003-4842-7751
Chung Yin KongDivision of General Internal Medicine, Department of Medicine, Mount Sinai Hospital, New York, New York (C.Y.K.).ORCID 0000-0001-6431-7830
Rafael MezaDepartment of Epidemiology, University of Michigan, Ann Arbor, Michigan, and Department of Integrative Oncology, BC Cancer Research Institute, British Columbia, Canada (R.M.).ORCID 0000-0002-1076-5037
Harry J de KoningErasmus MC-University Medical Center, Rotterdam, the Netherlands (K. de N., K. ten H., H.J. de K.).ORCID 0000-0003-4682-3646
Sylvia K PlevritisDepartment of Biomedical Data Sciences, Stanford University, Stanford, California (S.K.P.).
Summer S HanQuantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, California (S.S.H.).ORCID 0000-0002-2013-6883

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
Comparative Modeling of Lung Cancer Prevention and Control PoliciesU01CA199284 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DE KONING, HARRY J, HOLFORD, THEODORE R · 2015 to 2019
$8.4M
NCI NIH HHS U01 CA199284NCI NIH HHS U01 CA253858
6 · The paper itself

Abstract

backgroundIn their 2021 lung cancer screening recommendation update, the U.S. Preventive Services Task Force (USPSTF) evaluated strategies that select people based on their personal lung cancer risk (risk model-based strategies), highlighting the need for further research on the benefits and harms of risk model-based screening.

objectiveTo evaluate and compare the cost-effectiveness of risk model-based lung cancer screening strategies versus the USPSTF recommendation and to explore optimal risk thresholds.

designComparative modeling analysis. DATA SOURCES: National Lung Screening Trial; Surveillance, Epidemiology, and End Results program; U.S. Smoking History Generator. TARGET POPULATION: 1960 U.S. birth cohort. TIME HORIZON: 45 years. PERSPECTIVE: U.S. health care sector.

interventionAnnual low-dose computed tomography in risk model-based strategies that start screening at age 50 or 55 years, stop screening at age 80 years, with 6-year risk thresholds between 0.5% and 2.2% using the PLCOm2012 model. OUTCOME MEASURES: Incremental cost-effectiveness ratio (ICER) and cost-effectiveness efficiency frontier connecting strategies with the highest health benefit at a given cost. RESULTS OF BASE-CASE ANALYSIS: Risk model-based screening strategies were more cost-effective than the USPSTF recommendation and exclusively comprised the cost-effectiveness efficiency frontier. Among the strategies on the efficiency frontier, those with a 6-year risk threshold of 1.2% or greater were cost-effective with an ICER less than $100 000 per quality-adjusted life-year (QALY). Specifically, the strategy with a 1.2% risk threshold had an ICER of $94 659 (model range, $72 639 to $156 774), yielding more QALYs for less cost than the USPSTF recommendation, while having a similar level of screening coverage (person ever-screened 21.7% vs. USPSTF's 22.6%). RESULTS OF SENSITIVITY ANALYSES: Risk model-based strategies were robustly more cost-effective than the 2021 USPSTF recommendation under varying modeling assumptions. LIMITATION: Risk models were restricted to age, sex, and smoking-related risk predictors.

conclusionRisk model-based screening is more cost-effective than the USPSTF recommendation, thus warranting further consideration. PRIMARY FUNDING SOURCE: National Cancer Institute (NCI).

Indexed as

Lung NeoplasmsAged, 80 and overCost-Benefit AnalysisCost-Effectiveness AnalysisEarly Detection of CancerHumansLungMass ScreeningMiddle AgedQuality-Adjusted Life Years

Identifiers

PMID36745885
PMCPMC11025620

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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.