Literature DB >> 34383299

A risk-based framework for assessing real-time lung cancer screening eligibility that incorporates life expectancy and past screening findings.

Iakovos Toumazis1,2, Oguzhan Alagoz3, Ann Leung2, Sylvia K Plevritis1,2.   

Abstract

BACKGROUND: Current lung cancer risk-based screening approaches use a single risk-threshold, disregard life-expectancy, and ignore past screening findings. We address these limitations with a comprehensive analytical framework, the individualized lung cancer screening decision (ENGAGE) tool that aims to optimize lung cancer screening for US ever-smokers under dynamic risk assessment by incorporating life expectancy and past screening findings over time.
METHODS: ENGAGE employs a partially observable Markov decision process framework that integrates published risk prediction and disease progression models, to dynamically assess the trade-off between the expected health benefits and harms associated with screening. ENGAGE evaluates lung cancer risk annually and provides real-time screening eligibility that maximizes the expected quality-adjusted life-years (QALYs) of ever-smokers. We compare ENGAGE against the 2013 U.S. Preventive Services Task Force (USPSTF) lung cancer screening guideline and single-threshold risk-based screening paradigms.
RESULTS: Compared with the 2013 USPSTF guidelines, ENGAGE expands screening coverage among ever-smokers (ENGAGE: 78%, USPSTF: 61%), while reducing the number of screening examinations per person (ENGAGE:10.43, USPSTF:12.07, P < .001), yields higher effectiveness in terms of increased lung cancer-specific mortality reduction (ENGAGE: 19%, USPSTF: 15%, P < .001) and improves screening efficiency (ENGAGE: 696, USPSTF: 819 screens per death avoided, P < .001). When compared against a single-threshold risk-based screening strategy, ENGAGE increases QALY requiring 30% fewer screens per death avoided (ENGAGE: 696, single-threshold: 889, P < .001), and reduces false positives by 40%.
CONCLUSIONS: ENGAGE provides a comprehensive framework for dynamic risk-based assessment of lung cancer screening eligibility by incorporating life expectancy and past screening findings that can serve to guide future policies on the effectiveness and efficiency of screening. LAY
SUMMARY: A novel decision-analytical screening framework was developed for lung cancer, the individualized lung cancer screening decision (ENGAGE) tool to provide personalized screening schedules for ever-smokers. ENGAGE captures the dynamic nature of lung cancer risk and incorporates life expectancy into the screening decision-making process. ENGAGE integrates past screening findings and changes in smoking behavior of individuals and provides informed screening decisions that outperform existing screening guidelines and single-threshold risk-based screening approaches. A personalized lung cancer screening program facilitated by a tool such as ENGAGE could enhance the efficiency of lung cancer screening.
© 2021 American Cancer Society.

Entities:  

Keywords:  life expectancy; low-dose computed tomography; lung cancer; lung cancer risk; medical decision-making; partially observable Markov decision process (POMDP); personalized risk assessment; risk factors; risk prediction; risk-based screening; screening; smoking; uncertainty

Mesh:

Year:  2021        PMID: 34383299      PMCID: PMC8578300          DOI: 10.1002/cncr.33835

Source DB:  PubMed          Journal:  Cancer        ISSN: 0008-543X            Impact factor:   6.860


  38 in total

Review 1.  Pairing smoking-cessation services with lung cancer screening: A clinical guideline from the Association for the Treatment of Tobacco Use and Dependence and the Society for Research on Nicotine and Tobacco.

Authors:  Lisa M Fucito; Sharon Czabafy; Peter S Hendricks; Chris Kotsen; Donna Richardson; Benjamin A Toll
Journal:  Cancer       Date:  2016-02-24       Impact factor: 6.860

Review 2.  Recommendations of the Panel on Cost-effectiveness in Health and Medicine.

Authors:  M C Weinstein; J E Siegel; M R Gold; M S Kamlet; L B Russell
Journal:  JAMA       Date:  1996-10-16       Impact factor: 56.272

3.  Biennial lung cancer screening in Canada with smoking cessation-outcomes and cost-effectiveness.

Authors:  John R Goffin; William M Flanagan; Anthony B Miller; Natalie R Fitzgerald; Saima Memon; Michael C Wolfson; William K Evans
Journal:  Lung Cancer       Date:  2016-09-28       Impact factor: 5.705

4.  Cost-effectiveness of computed tomography screening for lung cancer in the United States.

Authors:  Pamela M McMahon; Chung Yin Kong; Colleen Bouzan; Milton C Weinstein; Lauren E Cipriano; Angela C Tramontano; Bruce E Johnson; Jane C Weeks; G Scott Gazelle
Journal:  J Thorac Oncol       Date:  2011-11       Impact factor: 15.609

5.  Baseline characteristics of participants in the randomized national lung screening trial.

Authors:  Denise R Aberle; Amanda M Adams; Christine D Berg; Jonathan D Clapp; Kathy L Clingan; Ilana F Gareen; David A Lynch; Pamela M Marcus; Paul F Pinsky
Journal:  J Natl Cancer Inst       Date:  2010-11-22       Impact factor: 13.506

6.  Comparative analysis of 5 lung cancer natural history and screening models that reproduce outcomes of the NLST and PLCO trials.

Authors:  Rafael Meza; Kevin ten Haaf; Chung Yin Kong; Ayca Erdogan; William C Black; Martin C Tammemagi; Sung Eun Choi; Jihyoun Jeon; Summer S Han; Vidit Munshi; Joost van Rosmalen; Paul Pinsky; Pamela M McMahon; Harry J de Koning; Eric J Feuer; William D Hazelton; Sylvia K Plevritis
Journal:  Cancer       Date:  2014-02-27       Impact factor: 6.860

7.  Chapter 4: Development of the counterfactual smoking histories used to assess the effects of tobacco control.

Authors:  Theodore R Holford; Lauren Clark
Journal:  Risk Anal       Date:  2012-07       Impact factor: 4.000

Review 8.  Lung Cancer Screening, Version 3.2018, NCCN Clinical Practice Guidelines in Oncology.

Authors:  Douglas E Wood; Ella A Kazerooni; Scott L Baum; George A Eapen; David S Ettinger; Lifang Hou; David M Jackman; Donald Klippenstein; Rohit Kumar; Rudy P Lackner; Lorriana E Leard; Inga T Lennes; Ann N C Leung; Samir S Makani; Pierre P Massion; Peter Mazzone; Robert E Merritt; Bryan F Meyers; David E Midthun; Sudhakar Pipavath; Christie Pratt; Chakravarthy Reddy; Mary E Reid; Arnold J Rotter; Peter B Sachs; Matthew B Schabath; Mark L Schiebler; Betty C Tong; William D Travis; Benjamin Wei; Stephen C Yang; Kristina M Gregory; Miranda Hughes
Journal:  J Natl Compr Canc Netw       Date:  2018-04       Impact factor: 11.908

9.  Risk prediction models for selection of lung cancer screening candidates: A retrospective validation study.

Authors:  Kevin Ten Haaf; Jihyoun Jeon; Martin C Tammemägi; Summer S Han; Chung Yin Kong; Sylvia K Plevritis; Eric J Feuer; Harry J de Koning; Ewout W Steyerberg; Rafael Meza
Journal:  PLoS Med       Date:  2017-04-04       Impact factor: 11.069

10.  Development and Validation of a Multivariable Lung Cancer Risk Prediction Model That Includes Low-Dose Computed Tomography Screening Results: A Secondary Analysis of Data From the National Lung Screening Trial.

Authors:  Martin C Tammemägi; Kevin Ten Haaf; Iakovos Toumazis; Chung Yin Kong; Summer S Han; Jihyoun Jeon; John Commins; Thomas Riley; Rafael Meza
Journal:  JAMA Netw Open       Date:  2019-03-01
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  1 in total

1.  Evaluation of benefits and harms of adaptive screening schedules for lung cancer: A microsimulation study.

Authors:  Pianpian Cao; Jihyoun Jeon; Rafael Meza
Journal:  J Med Screen       Date:  2022-08-22       Impact factor: 1.687

  1 in total

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