Prevalence of incidental breast cancer and precursor lesions in autopsy studies: a systematic review and meta-analysis
Access status:
Open Access
Type
ArticleAuthor/s
Thomas, Elizabeth TDel Mar, Chris
Glasziou, Paul
Wright, Gordon
Barratt, Alexandra L
Bell, Katy J.L.
Abstract
Autopsy studies demonstrate the prevalence pool of incidental breast cancer in the population, but estimates are uncertain due to small numbers in any primary study. The aim was to conduct a systematic review of autopsy studies to estimate the prevalence of incidental breast cancer ...
See moreAutopsy studies demonstrate the prevalence pool of incidental breast cancer in the population, but estimates are uncertain due to small numbers in any primary study. The aim was to conduct a systematic review of autopsy studies to estimate the prevalence of incidental breast cancer and precursors. Relevant articles were identified through searching PubMed and Embase from inception up to April 2016, and backward and forward citations. We included autopsy studies of women with no history of breast pathology, which included systematic histological examination of at least one breast, and which allowed calculation of the prevalence of incidental breast cancer or precursor lesions. Data were pooled using logistic regression models with random intercepts (non-linear mixed models).
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See moreAutopsy studies demonstrate the prevalence pool of incidental breast cancer in the population, but estimates are uncertain due to small numbers in any primary study. The aim was to conduct a systematic review of autopsy studies to estimate the prevalence of incidental breast cancer and precursors. Relevant articles were identified through searching PubMed and Embase from inception up to April 2016, and backward and forward citations. We included autopsy studies of women with no history of breast pathology, which included systematic histological examination of at least one breast, and which allowed calculation of the prevalence of incidental breast cancer or precursor lesions. Data were pooled using logistic regression models with random intercepts (non-linear mixed models).
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Date
2017Source title
BMC cancerVolume
17Publisher
Springer NatureLicence
Creative Commons Attribution 4.0Faculty/School
Faculty of Medicine and Health, Sydney School of Public HealthShare