학술논문
The Impact of Model Assumptions on Personalized Lung Cancer Screening Recommendations.
Document Type
Academic Journal
Author
Ten Haaf K; Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.; de Nijs K; Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.; Simoni G; Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA.; Alban A; MGH Institute for Technology Assessment, Harvard Medical School, Boston, MA, USA.; Cao P; Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA.; Sun Z; Canadian Partnership Against Cancer, Toronto, ON, Canada.; Yong J; Canadian Partnership Against Cancer, Toronto, ON, Canada.; Jeon J; Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA.; Toumazis I; Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.; Han SS; Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA, USA.; Gazelle GS; Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.; Kong CY; Division of General Internal Medicine, Department of Medicine, Mount Sinai Hospital, New York, NY, USA.; Plevritis SK; Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA.; Meza R; Department of Integrative Oncology, BC Cancer Research Institute, BC, Canada.; School of Population and Public Health, University of British Columbia, BC, Canada.; de Koning HJ; Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.
Source
Publisher: Sage Publications Country of Publication: United States NLM ID: 8109073 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1552-681X (Electronic) Linking ISSN: 0272989X NLM ISO Abbreviation: Med Decis Making Subsets: MEDLINE
Subject
Language
English
Abstract
Background: Recommendations regarding personalized lung cancer screening are being informed by natural-history modeling. Therefore, understanding how differences in model assumptions affect model-based personalized screening recommendations is essential.
Design: Five Cancer Intervention and Surveillance Modeling Network (CISNET) models were evaluated. Lung cancer incidence, mortality, and stage distributions were compared across 4 theoretical scenarios to assess model assumptions regarding 1) sojourn times, 2) stage-specific sensitivities, and 3) screening-induced lung cancer mortality reductions. Analyses were stratified by sex and smoking behavior.
Results: Most cancers had sojourn times <5 y (model range [MR]; lowest to highest value across models: 83.5%-98.7% of cancers). However, cancer aggressiveness still varied across models, as demonstrated by differences in proportions of cancers with sojourn times <2 y (MR: 42.5%-64.6%) and 2 to 4 y (MR: 28.8%-43.6%). Stage-specific sensitivity varied, particularly for stage I (MR: 31.3%-91.5%). Screening reduced stage IV incidence in most models for 1 y postscreening; increased sensitivity prolonged this period to 2 to 5 y. Screening-induced lung cancer mortality reductions among lung cancers detected at screening ranged widely (MR: 14.6%-48.9%), demonstrating variations in modeled treatment effectiveness of screen-detected cases. All models assumed longer sojourn times and greater screening-induced lung cancer mortality reductions for women. Models assuming differences in cancer epidemiology by smoking behaviors assumed shorter sojourn times and lower screening-induced lung cancer mortality reductions for heavy smokers.
Conclusions: Model-based personalized screening recommendations are primarily driven by assumptions regarding sojourn times (favoring longer intervals for groups more likely to develop less aggressive cancers), sensitivity (higher sensitivities favoring longer intervals), and screening-induced mortality reductions (greater reductions favoring shorter intervals).
Implications: Models suggest longer screening intervals may be feasible and benefits may be greater for women and light smokers.
Highlights: Natural-history models are increasingly used to inform lung cancer screening, but causes for variations between models are difficult to assess.This is the first evaluation of these causes and their impact on personalized screening recommendations through easily interpretable metrics.Models vary regarding sojourn times, stage-specific sensitivities, and screening-induced lung cancer mortality reductions.Model outcomes were similar in predicting greater screening benefits for women and potentially light smokers. Longer screening intervals may be feasible for women and light smokers.
Competing Interests: The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Kevin ten Haaf reports grants from NIH, the Dutch Research Council, the EU, University of Zurich, Cancer Research UK, Cancer Australia, the Medical Services Advisory Committee of the Australian Ministry of Health and Erasmus University Medical Center Rotterdam/Erasmus University Rotterdam/Technical University of Delft; honoraria from CHUV: Centre Hospitalier Universitaire Vaudois and Johnson&Johnson paid to his institution and support for traveling and lodging fees from CHUV and Rescue Lung Society. Koen de Nijs reports grants from NIH, the EU, and University of Zurich; Pianpian Cao reports grants from NIH; Iakovos Toumazis reports grants from NIH; G. Scott Gazelle reports grants from NIH; Chung Ying Kong reports grants from NIH; Sylvia Plevritis reports grants from NIH, NCI, and ACED, consulting fees from Adela Biosciences, support from attending meetings and/or travel from NCI and advisory board memberships for NCI, Knight Cancer Center, and the Ontario Institute for Cancer Research; Rafael Meza reports grants from NIH; Harry de Koning reports honoraria for lectures for TEVA/Menarini/Astra Zeneca and external reviews for Bayer. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Financial support for this study was provided in part by grants U01CA199284 from the National Cancer Institute (NCI) and VENI grant from the Dutch Research Council/Netherlands Organization of Health Research (ZonMW) (grant No. 09150161910060). The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report. The statements contained herein are solely those of the authors and do not represent or imply concurrence or endorsement by NCI. OncoSim is led and supported by the Canadian Partnership Against Cancer, with model development by Statistics Canada, and is made possible through funding from Health Canada. The assumptions and calculations underlying the simulation results were prepared by the authors, and the responsibility for the use and interpretation of these data and their reporting is entirely that of the authors.
Design: Five Cancer Intervention and Surveillance Modeling Network (CISNET) models were evaluated. Lung cancer incidence, mortality, and stage distributions were compared across 4 theoretical scenarios to assess model assumptions regarding 1) sojourn times, 2) stage-specific sensitivities, and 3) screening-induced lung cancer mortality reductions. Analyses were stratified by sex and smoking behavior.
Results: Most cancers had sojourn times <5 y (model range [MR]; lowest to highest value across models: 83.5%-98.7% of cancers). However, cancer aggressiveness still varied across models, as demonstrated by differences in proportions of cancers with sojourn times <2 y (MR: 42.5%-64.6%) and 2 to 4 y (MR: 28.8%-43.6%). Stage-specific sensitivity varied, particularly for stage I (MR: 31.3%-91.5%). Screening reduced stage IV incidence in most models for 1 y postscreening; increased sensitivity prolonged this period to 2 to 5 y. Screening-induced lung cancer mortality reductions among lung cancers detected at screening ranged widely (MR: 14.6%-48.9%), demonstrating variations in modeled treatment effectiveness of screen-detected cases. All models assumed longer sojourn times and greater screening-induced lung cancer mortality reductions for women. Models assuming differences in cancer epidemiology by smoking behaviors assumed shorter sojourn times and lower screening-induced lung cancer mortality reductions for heavy smokers.
Conclusions: Model-based personalized screening recommendations are primarily driven by assumptions regarding sojourn times (favoring longer intervals for groups more likely to develop less aggressive cancers), sensitivity (higher sensitivities favoring longer intervals), and screening-induced mortality reductions (greater reductions favoring shorter intervals).
Implications: Models suggest longer screening intervals may be feasible and benefits may be greater for women and light smokers.
Highlights: Natural-history models are increasingly used to inform lung cancer screening, but causes for variations between models are difficult to assess.This is the first evaluation of these causes and their impact on personalized screening recommendations through easily interpretable metrics.Models vary regarding sojourn times, stage-specific sensitivities, and screening-induced lung cancer mortality reductions.Model outcomes were similar in predicting greater screening benefits for women and potentially light smokers. Longer screening intervals may be feasible for women and light smokers.
Competing Interests: The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Kevin ten Haaf reports grants from NIH, the Dutch Research Council, the EU, University of Zurich, Cancer Research UK, Cancer Australia, the Medical Services Advisory Committee of the Australian Ministry of Health and Erasmus University Medical Center Rotterdam/Erasmus University Rotterdam/Technical University of Delft; honoraria from CHUV: Centre Hospitalier Universitaire Vaudois and Johnson&Johnson paid to his institution and support for traveling and lodging fees from CHUV and Rescue Lung Society. Koen de Nijs reports grants from NIH, the EU, and University of Zurich; Pianpian Cao reports grants from NIH; Iakovos Toumazis reports grants from NIH; G. Scott Gazelle reports grants from NIH; Chung Ying Kong reports grants from NIH; Sylvia Plevritis reports grants from NIH, NCI, and ACED, consulting fees from Adela Biosciences, support from attending meetings and/or travel from NCI and advisory board memberships for NCI, Knight Cancer Center, and the Ontario Institute for Cancer Research; Rafael Meza reports grants from NIH; Harry de Koning reports honoraria for lectures for TEVA/Menarini/Astra Zeneca and external reviews for Bayer. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Financial support for this study was provided in part by grants U01CA199284 from the National Cancer Institute (NCI) and VENI grant from the Dutch Research Council/Netherlands Organization of Health Research (ZonMW) (grant No. 09150161910060). The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report. The statements contained herein are solely those of the authors and do not represent or imply concurrence or endorsement by NCI. OncoSim is led and supported by the Canadian Partnership Against Cancer, with model development by Statistics Canada, and is made possible through funding from Health Canada. The assumptions and calculations underlying the simulation results were prepared by the authors, and the responsibility for the use and interpretation of these data and their reporting is entirely that of the authors.