Literature DB >> 29554468

Comparing CISNET Breast Cancer Incidence and Mortality Predictions to Observed Clinical Trial Results of Mammography Screening from Ages 40 to 49.

Jeroen J van den Broek1, Nicolien T van Ravesteyn1, Jeanne S Mandelblatt2, Hui Huang3, Mehmet Ali Ergun4, Elizabeth S Burnside5, Cong Xu6, Yisheng Li7, Oguzhan Alagoz4, Sandra J Lee3, Natasha K Stout8, Juhee Song7, Amy Trentham-Dietz4, Sylvia K Plevritis6, Sue M Moss9, Harry J de Koning1.   

Abstract

BACKGROUND: The UK Age trial compared annual mammography screening of women ages 40 to 49 years with no screening and found a statistically significant breast cancer mortality reduction at the 10-year follow-up but not at the 17-year follow-up. The objective of this study was to compare the observed Age trial results with the Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer model predicted results.
METHODS: Five established CISNET breast cancer models used data on population demographics, screening attendance, and mammography performance from the Age trial together with extant natural history parameters to project breast cancer incidence and mortality in the control and intervention arm of the trial.
RESULTS: The models closely reproduced the effect of annual screening from ages 40 to 49 years on breast cancer incidence. Restricted to breast cancer deaths originating from cancers diagnosed during the intervention phase, the models estimated an average 15% (range across models, 13% to 17%) breast cancer mortality reduction at the 10-year follow-up compared with 25% (95% CI, 3% to 42%) observed in the trial. At the 17-year follow-up, the models predicted 13% (range, 10% to 17%) reduction in breast cancer mortality compared with the non-significant 12% (95% CI, -4% to 26%) in the trial.
CONCLUSIONS: The models underestimated the effect of screening on breast cancer mortality at the 10-year follow-up. Overall, the models captured the observed long-term effect of screening from age 40 to 49 years on breast cancer incidence and mortality in the UK Age trial, suggesting that the model structures, input parameters, and assumptions about breast cancer natural history are reasonable for estimating the impact of screening on mortality in this age group.

Entities:  

Keywords:  CISNET; breast cancer models; external validation; mammography trial simulation

Mesh:

Substances:

Year:  2018        PMID: 29554468      PMCID: PMC5862071          DOI: 10.1177/0272989X17718168

Source DB:  PubMed          Journal:  Med Decis Making        ISSN: 0272-989X            Impact factor:   2.583


  24 in total

1.  Collaborative Modeling of the Benefits and Harms Associated With Different U.S. Breast Cancer Screening Strategies.

Authors:  Jeanne S Mandelblatt; Natasha K Stout; Clyde B Schechter; Jeroen J van den Broek; Diana L Miglioretti; Martin Krapcho; Amy Trentham-Dietz; Diego Munoz; Sandra J Lee; Donald A Berry; Nicolien T van Ravesteyn; Oguzhan Alagoz; Karla Kerlikowske; Anna N A Tosteson; Aimee M Near; Amanda Hoeffken; Yaojen Chang; Eveline A Heijnsdijk; Gary Chisholm; Xuelin Huang; Hui Huang; Mehmet Ali Ergun; Ronald Gangnon; Brian L Sprague; Sylvia Plevritis; Eric Feuer; Harry J de Koning; Kathleen A Cronin
Journal:  Ann Intern Med       Date:  2016-01-12       Impact factor: 25.391

2.  Screening for Breast Cancer: U.S. Preventive Services Task Force Recommendation Statement.

Authors:  Albert L Siu
Journal:  Ann Intern Med       Date:  2016-01-12       Impact factor: 25.391

3.  Randomized controlled trial of mammographic screening from age 40 ('Age' trial): patterns of screening attendance.

Authors:  L E Johns; S M Moss
Journal:  J Med Screen       Date:  2010       Impact factor: 2.136

4.  False-positive results in the randomized controlled trial of mammographic screening from age 40 ("Age" trial).

Authors:  Louise E Johns; Sue M Moss
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2010-09-13       Impact factor: 4.254

5.  Tipping the balance of benefits and harms to favor screening mammography starting at age 40 years: a comparative modeling study of risk.

Authors:  Nicolien T van Ravesteyn; Diana L Miglioretti; Natasha K Stout; Sandra J Lee; Clyde B Schechter; Diana S M Buist; Hui Huang; Eveline A M Heijnsdijk; Amy Trentham-Dietz; Oguzhan Alagoz; Aimee M Near; Karla Kerlikowske; Heidi D Nelson; Jeanne S Mandelblatt; Harry J de Koning
Journal:  Ann Intern Med       Date:  2012-05-01       Impact factor: 25.391

6.  Effect of mammographic screening from age 40 years on breast cancer mortality in the UK Age trial at 17 years' follow-up: a randomised controlled trial.

Authors:  Sue M Moss; Christopher Wale; Robert Smith; Andrew Evans; Howard Cuckle; Stephen W Duffy
Journal:  Lancet Oncol       Date:  2015-07-20       Impact factor: 41.316

7.  Prevalence of mammographically dense breasts in the United States.

Authors:  Brian L Sprague; Ronald E Gangnon; Veronica Burt; Amy Trentham-Dietz; John M Hampton; Robert D Wellman; Karla Kerlikowske; Diana L Miglioretti
Journal:  J Natl Cancer Inst       Date:  2014-09-12       Impact factor: 13.506

8.  Comparisons between different polychemotherapy regimens for early breast cancer: meta-analyses of long-term outcome among 100,000 women in 123 randomised trials.

Authors:  R Peto; C Davies; J Godwin; R Gray; H C Pan; M Clarke; D Cutter; S Darby; P McGale; C Taylor; Y C Wang; J Bergh; A Di Leo; K Albain; S Swain; M Piccart; K Pritchard
Journal:  Lancet       Date:  2011-12-05       Impact factor: 79.321

9.  Model transparency and validation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-7.

Authors:  David M Eddy; William Hollingworth; J Jaime Caro; Joel Tsevat; Kathryn M McDonald; John B Wong
Journal:  Med Decis Making       Date:  2012 Sep-Oct       Impact factor: 2.583

Review 10.  The benefits and harms of breast cancer screening: an independent review.

Authors: 
Journal:  Lancet       Date:  2012-10-30       Impact factor: 79.321

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  9 in total

1.  Clinical Benefits, Harms, and Cost-Effectiveness of Breast Cancer Screening for Survivors of Childhood Cancer Treated With Chest Radiation : A Comparative Modeling Study.

Authors:  Jennifer M Yeh; Kathryn P Lowry; Clyde B Schechter; Lisa R Diller; Oguzhan Alagoz; Gregory T Armstrong; John M Hampton; Wendy Leisenring; Qi Liu; Jeanne S Mandelblatt; Diana L Miglioretti; Chaya S Moskowitz; Kevin C Oeffinger; Amy Trentham-Dietz; Natasha K Stout
Journal:  Ann Intern Med       Date:  2020-07-07       Impact factor: 25.391

2.  Breast Cancer Screening Strategies for Women With ATM, CHEK2, and PALB2 Pathogenic Variants: A Comparative Modeling Analysis.

Authors:  Kathryn P Lowry; H Amarens Geuzinge; Natasha K Stout; Oguzhan Alagoz; John Hampton; Karla Kerlikowske; Harry J de Koning; Diana L Miglioretti; Nicolien T van Ravesteyn; Clyde Schechter; Brian L Sprague; Anna N A Tosteson; Amy Trentham-Dietz; Donald Weaver; Martin J Yaffe; Jennifer M Yeh; Fergus J Couch; Chunling Hu; Peter Kraft; Eric C Polley; Jeanne S Mandelblatt; Allison W Kurian; Mark E Robson
Journal:  JAMA Oncol       Date:  2022-04-01       Impact factor: 33.006

3.  Introduction to the Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Models.

Authors:  Oguzhan Alagoz; Donald A Berry; Harry J de Koning; Eric J Feuer; Sandra J Lee; Sylvia K Plevritis; Clyde B Schechter; Natasha K Stout; Amy Trentham-Dietz; Jeanne S Mandelblatt
Journal:  Med Decis Making       Date:  2018-04       Impact factor: 2.583

4.  Long-Term Outcomes and Cost-Effectiveness of Breast Cancer Screening With Digital Breast Tomosynthesis in the United States.

Authors:  Kathryn P Lowry; Amy Trentham-Dietz; Clyde B Schechter; Oguzhan Alagoz; William E Barlow; Elizabeth S Burnside; Emily F Conant; John M Hampton; Hui Huang; Karla Kerlikowske; Sandra J Lee; Diana L Miglioretti; Brian L Sprague; Anna N A Tosteson; Martin J Yaffe; Natasha K Stout
Journal:  J Natl Cancer Inst       Date:  2020-06-01       Impact factor: 13.506

Review 5.  Reflecting on 20 years of breast cancer modeling in CISNET: Recommendations for future cancer systems modeling efforts.

Authors:  Amy Trentham-Dietz; Oguzhan Alagoz; Christina Chapman; Xuelin Huang; Jinani Jayasekera; Nicolien T van Ravesteyn; Sandra J Lee; Clyde B Schechter; Jennifer M Yeh; Sylvia K Plevritis; Jeanne S Mandelblatt
Journal:  PLoS Comput Biol       Date:  2021-06-17       Impact factor: 4.475

6.  Breast Cancer Screening Among Childhood Cancer Survivors Treated Without Chest Radiation: Clinical Benefits and Cost-Effectiveness.

Authors:  Jennifer M Yeh; Kathryn P Lowry; Clyde B Schechter; Lisa R Diller; Grace O'Brien; Oguzhan Alagoz; Gregory T Armstrong; John M Hampton; Melissa M Hudson; Wendy Leisenring; Qi Liu; Jeanne S Mandelblatt; Diana L Miglioretti; Chaya S Moskowitz; Paul C Nathan; Joseph P Neglia; Kevin C Oeffinger; Amy Trentham-Dietz; Natasha K Stout
Journal:  J Natl Cancer Inst       Date:  2022-02-07       Impact factor: 11.816

7.  Trade-Offs Between Harms and Benefits of Different Breast Cancer Screening Intervals Among Low-Risk Women.

Authors:  Nicolien T van Ravesteyn; Clyde B Schechter; John M Hampton; Oguzhan Alagoz; Jeroen J van den Broek; Karla Kerlikowske; Jeanne S Mandelblatt; Diana L Miglioretti; Brian L Sprague; Natasha K Stout; Harry J de Koning; Amy Trentham-Dietz; Anna N A Tosteson
Journal:  J Natl Cancer Inst       Date:  2021-08-02       Impact factor: 13.506

8.  The OncoSim-Breast Cancer Microsimulation Model.

Authors:  Jean H E Yong; Claude Nadeau; William M Flanagan; Andrew J Coldman; Keiko Asakawa; Rochelle Garner; Natalie Fitzgerald; Martin J Yaffe; Anthony B Miller
Journal:  Curr Oncol       Date:  2022-03-03       Impact factor: 3.677

9.  Personalizing Breast Cancer Screening Based on Polygenic Risk and Family History.

Authors:  Jeroen J van den Broek; Clyde B Schechter; Nicolien T van Ravesteyn; A Cecile J W Janssens; Michael C Wolfson; Amy Trentham-Dietz; Jacques Simard; Douglas F Easton; Jeanne S Mandelblatt; Peter Kraft; Harry J de Koning
Journal:  J Natl Cancer Inst       Date:  2021-04-06       Impact factor: 11.816

  9 in total

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