The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
The Impact of Mammographic Screening on Breast Cancer Mortality in Europe: A Review of Observational Studies
Open accessAbstract
Objectives: To assess the impact of population-based mammographic screening on breast cancer mortality in Europe, considering different methodologies and limitations of the data. Methods: We conducted a systematic literature review of European trend studies ( n = 17), incidence-based mortality (IBM) studies ( n = 20) and case-control (CC) studies ( n = 8). Estimates of the reduction in breast cancer mortality for women invited versus not invited and/or for women screened versus not screened were obtained. The results of IBM studies and CC studies were each pooled using a random effects meta-analysis. Results: Twelve of the 17 trend studies quantified the impact of population-based screening on breast cancer mortality. The estimated breast cancer mortality reductions ranged from 1% to 9% per year in studies reporting an annual percentage change, and from 28% to 36% in those comparing post- and prescreening periods. In the IBM studies, the pooled mortality reduction was 25% (relative risk [RR] 0.75, 95% confidence interval [CI] 0.69–0.81) among invited women and 38% (RR 0.62, 95% CI 0.56–0.69) among those actually screened. The corresponding pooled estimates from the CC studies were 31% (odds ratio [OR] 0.69, 95% CI 0.57–0.83), and 48% (OR 0.52, 95% CI 0.42–0.65) adjusted for self-selection. Conclusions: Valid observational designs are those where sufficient longitudinal individual data are available, directly linking a woman's screening history to her cause of death. From such studies, the best ‘European’ estimate of breast cancer mortality reduction is 25–31% for women invited for screening, and 38–48% for women actually screened. Much of the current controversy on breast cancer screening is due to the use of inappropriate methodological approaches that are unable to capture the true effect of mammographic screening.
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Cites
Randomized, Controlled Trials, Observational Studies, and the Hierarchy of Research Designs
Concato, J., Shah, N., & Horwitz, R. I. (2000) · New England Journal of Medicine · doi:10.1056/nejm200006223422507 · Cited by 3,563
Systematic Reviews in Health Care : Meta-Analysis in Context
Egger, M., Smith, G. D., & Altman, D. G. (2000) · Cited by 2,312
Screening for breast cancer with mammography
Gøtzsche, P. C., & Jørgensen, K. J. (2013) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd001877.pub5 · Cited by 1,869
Systematic Reviews in Health Care: Meta‐Analysis in Context
(2001) · doi:10.1002/9780470693926 · Cited by 1,693
Screening for Breast Cancer: An Update for the U.S. Preventive Services Task Force
Nelson, H. D., Tyne, K., Naik, A., Bougatsos, C., Chan, B. K., & Humphrey, L. (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-151-10-200911170-00009 · Cited by 1,248
Long-term effects of mammography screening: updated overview of the Swedish randomised trials
Nyström, L., Andersson, I., Bjurstam, N., Frisell, J., Nordenskjöld, B., & Rutqvist, L. E. (2002) · The Lancet · doi:10.1016/s0140-6736(02)08020-0 · Cited by 1,183
Self-citationElmore, J. G. (2005) · JAMA · doi:10.1001/jama.293.10.1245 · Cited by 1,075
Is screening for breast cancer with mammography justifiable?
Gøtzsche, P. C., & Olsen, O. (2000) · The Lancet · doi:10.1016/s0140-6736(99)06065-1 · Cited by 853
IARC Handbooks of Cancer Prevention: Breast cancer screening
Feig, S. A. (2003) · Cancer · doi:10.1002/cncr.11334 · Cited by 742
Cochrane review on screening for breast cancer with mammography
Olsen, O., & Gøtzsche, P. C. (2001) · The Lancet · doi:10.1016/s0140-6736(01)06449-2 · Cited by 710
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Large scale deep learning for computer aided detection of mammographic lesions
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Smith, R. A., Andrews, K. S., Brooks, D., Fedewa, S. A., Manassaram‐Baptiste, D., Saslow, D., Brawley, O. W., & Wender, R. C. (2017) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21392 · Cited by 719
Detecting and classifying lesions in mammograms with Deep Learning
Ribli, D., Horváth, A., Unger, Z., Pollner, P., & Csabai, I. (2018) · Scientific Reports · doi:10.1038/s41598-018-22437-z · Cited by 696
Rodriguez-Ruiz, A., Lång, K., Gubern-Merida, A., Broeders, M., Gennaro, G., Clauser, P., Helbich, T. H., Chevalier, M., Tan, T., Mertelmeier, T., et al. (2019) · JNCI: Journal of the National Cancer Institute · doi:10.1093/jnci/djy222 · Cited by 692
Self-citationDetection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System
Rodríguez-Ruiz, A., Krupinski, E., Mordang, J.-J., Schilling, K., Heywang-Köbrunner, S. H., Sechopoulos, I., & Mann, R. M. (2019) · Radiology · doi:10.1148/radiol.2018181371 · Cited by 602
Metadata
- Authors
- Broeders, M., Moss, S., Nyström, L., Njor, S., Jonsson, H., Paap, E., Massat, N., Duffy, S., Lynge, E., & Paci, E.
- Venue
- Journal of Medical Screening
- Publisher
- SAGE Publications
- Published
- 2012-09
- Volume
- 19
- Issue
- 1_suppl
- Pages
- 14-25
- ISSN
- 0969-1413 (ISSN-L)
- 1475-5793
- OpenAlex
- W2100940783
- Cited by
- 489
- References
- 48
- FWCI
- 14.49
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Broeders, M., Moss, S., Nyström, L., Njor, S., Jonsson, H., Paap, E., Massat, N., Duffy, S., Lynge, E., & Paci, E. (2012). The Impact of Mammographic Screening on Breast Cancer Mortality in Europe: A Review of Observational Studies. Journal of Medical Screening. https://doi.org/10.1258/jms.2012.012078
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