Literature DB >> 33872390

Breast cancer screening and overdiagnosis.

Jean-Luc Bulliard1, Anna-Belle Beau2, Sisse Njorv3,4, Wendy Yi-Ying Wu5, Pietro Procopio6,7, Carolyn Nickson6,7, Elsebeth Lynge8.   

Abstract

Overdiagnosis is a harmful consequence of screening which is particularly challenging to estimate. An unbiased setting to measure overdiagnosis in breast cancer screening requires comparative data from a screened and an unscreened cohort for at least 30 years. Such randomized data will not become available, leaving us with observational data over shorter time periods and outcomes of modelling. This collaborative effort of the International Cancer Screening Network quantified the variation in estimated breast cancer overdiagnosis in organized programs with evaluation of both observed and simulated data, and presented examples of how modelling can provide additional insights. Reliable observational data, analysed with study design accounting for methodological pitfalls, and modelling studies with different approaches, indicate that overdiagnosis accounts for less than 10% of invasive breast cancer cases in a screening target population of women aged 50 to 69. Estimates above this level are likely to derive from inaccuracies in study design. The widely discrepant estimates of overdiagnosis reported from observational data could substantially be reduced by use of a cohort study design with at least 10 years of follow-up after screening stops. In contexts where concomitant opportunistic screening or gradual implementation of screening occurs, and data on valid comparison groups are not readily available, modelling of screening intervention becomes an advantageous option to obtain reliable estimates of breast cancer overdiagnosis. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Entities:  

Keywords:  breast cancer; estimation; methodology; overdiagnosis; screening

Year:  2021        PMID: 33872390     DOI: 10.1002/ijc.33602

Source DB:  PubMed          Journal:  Int J Cancer        ISSN: 0020-7136            Impact factor:   7.396


  8 in total

1.  Artificial Intelligence Evaluation of 122 969 Mammography Examinations from a Population-based Screening Program.

Authors:  Marthe Larsen; Camilla F Aglen; Christoph I Lee; Solveig R Hoff; Håkon Lund-Hanssen; Kristina Lång; Jan F Nygård; Giske Ursin; Solveig Hofvind
Journal:  Radiology       Date:  2022-03-29       Impact factor: 29.146

2.  Estimation of Breast Cancer Overdiagnosis in a U.S. Breast Screening Cohort.

Authors:  Marc D Ryser; Jane Lange; Lurdes Y T Inoue; Ellen S O'Meara; Charlotte Gard; Diana L Miglioretti; Jean-Luc Bulliard; Andrew F Brouwer; E Shelley Hwang; Ruth B Etzioni
Journal:  Ann Intern Med       Date:  2022-03-01       Impact factor: 51.598

3.  Identification of women at risk of hereditary breast-ovarian cancer among participants in a population-based breast cancer screening.

Authors:  Luigina Bonelli; Ivana Valle; Ivana Rebora; Paola Ricci; Lidia Biocchi; Giovanna Bruschi; Sabrina Parodi; Carla Bruzzone; Liliana Varesco
Journal:  Fam Cancer       Date:  2021-10-20       Impact factor: 2.446

4.  Burden of All Cancers Along With Attributable Risk Factors in China From 1990 to 2019: Comparison With Japan, European Union, and USA.

Authors:  Xiaorong Yang; Hui Chen; Shaowei Sang; Hao Chen; Lanbo Li; Xiaoyun Yang
Journal:  Front Public Health       Date:  2022-05-26

5.  Machine learning-based diagnostic evaluation of shear-wave elastography in BI-RADS category 4 breast cancer screening: a multicenter, retrospective study.

Authors:  Yi Tang; Minjie Liang; Li Tao; Minjun Deng; Tianfu Li
Journal:  Quant Imaging Med Surg       Date:  2022-02

6.  Costs and Effects of Implementing Digital Tomosynthesis in a Population-Based Breast Cancer Screening Program: Predictions Using Results from the To-Be Trial in Norway.

Authors:  Tron Anders Moger; Åsne Holen; Berit Hanestad; Solveig Hofvind
Journal:  Pharmacoecon Open       Date:  2022-07-07

Review 7.  Field cancerization in breast cancer.

Authors:  Emanuela Gadaleta; Graeme J Thorn; Helen Ross-Adams; Louise J Jones; Claude Chelala
Journal:  J Pathol       Date:  2022-05-03       Impact factor: 9.883

8.  Reply to: Comments on "Finding the optimal mammography screening strategy: A cost-effectiveness analysis of 920 modeled strategies".

Authors:  Lindy M Kregting; Valérie D V Sankatsing; Eveline A M Heijnsdijk; Harry J de Koning; Nicolien T van Ravesteyn
Journal:  Int J Cancer       Date:  2022-05-06       Impact factor: 7.316

  8 in total

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