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Predictors of mammographic microcalcifications

We examined the association between established risk factors for breast cancer and microcalcification clusters and their asymmetry. A cohort study of 53 273 Swedish women aged 30 to 80 years, with comprehensive information on breast cancer risk factors and mammograms, was conducted. Total number of...

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Autores principales: Azam, Shadi, Eriksson, Mikael, Sjölander, Arvid, Gabrielson, Marike, Hellgren, Roxanna, Czene, Kamila, Hall, Per
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7821182/
https://www.ncbi.nlm.nih.gov/pubmed/32949149
http://dx.doi.org/10.1002/ijc.33302
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author Azam, Shadi
Eriksson, Mikael
Sjölander, Arvid
Gabrielson, Marike
Hellgren, Roxanna
Czene, Kamila
Hall, Per
author_facet Azam, Shadi
Eriksson, Mikael
Sjölander, Arvid
Gabrielson, Marike
Hellgren, Roxanna
Czene, Kamila
Hall, Per
author_sort Azam, Shadi
collection PubMed
description We examined the association between established risk factors for breast cancer and microcalcification clusters and their asymmetry. A cohort study of 53 273 Swedish women aged 30 to 80 years, with comprehensive information on breast cancer risk factors and mammograms, was conducted. Total number of microcalcification clusters and the average mammographic density area were measured using a Computer Aided Detection system and the STRATUS method, respectively. A polygenic risk score for breast cancer, including 313 single nucleotide polymorphisms, was calculated for those women genotyped (N = 7387). Odds ratios (ORs) and 95% confidence intervals (CIs), with adjustment for potential confounders, were estimated. Age was strongly associated with microcalcification clusters. Both high mammographic density (>40 cm(2)), and high polygenic risk score (80‐100 percentile) were associated with microcalcification clusters, OR = 2.08 (95% CI = 1.93‐2.25) and OR = 1.22 (95% CI = 1.06‐1.48), respectively. Among reproductive risk factors, life‐time breastfeeding duration >1 year was associated with microcalcification clusters OR = 1.22 (95% CI = 1.03‐1.46). The association was confined to postmenopausal women. Among lifestyle risk factors, women with a body mass index ≥30 kg/m(2) had the lowest risk of microcalcification clusters OR = 0.79 (95% CI = 0.73‐0.85) and the association was stronger among premenopausal women. Our results suggest that age, mammographic density, genetic predictors of breast cancer, having more than two children, longer duration of breast‐feeding are significantly associated with increased risk of microcalcification clusters. However, most lifestyle risk factors for breast cancer seem to protect against presence of microcalcification clusters. More research is needed to study biological mechanisms behind microcalcifications formation.
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spelling pubmed-78211822021-01-26 Predictors of mammographic microcalcifications Azam, Shadi Eriksson, Mikael Sjölander, Arvid Gabrielson, Marike Hellgren, Roxanna Czene, Kamila Hall, Per Int J Cancer Cancer Epidemiology We examined the association between established risk factors for breast cancer and microcalcification clusters and their asymmetry. A cohort study of 53 273 Swedish women aged 30 to 80 years, with comprehensive information on breast cancer risk factors and mammograms, was conducted. Total number of microcalcification clusters and the average mammographic density area were measured using a Computer Aided Detection system and the STRATUS method, respectively. A polygenic risk score for breast cancer, including 313 single nucleotide polymorphisms, was calculated for those women genotyped (N = 7387). Odds ratios (ORs) and 95% confidence intervals (CIs), with adjustment for potential confounders, were estimated. Age was strongly associated with microcalcification clusters. Both high mammographic density (>40 cm(2)), and high polygenic risk score (80‐100 percentile) were associated with microcalcification clusters, OR = 2.08 (95% CI = 1.93‐2.25) and OR = 1.22 (95% CI = 1.06‐1.48), respectively. Among reproductive risk factors, life‐time breastfeeding duration >1 year was associated with microcalcification clusters OR = 1.22 (95% CI = 1.03‐1.46). The association was confined to postmenopausal women. Among lifestyle risk factors, women with a body mass index ≥30 kg/m(2) had the lowest risk of microcalcification clusters OR = 0.79 (95% CI = 0.73‐0.85) and the association was stronger among premenopausal women. Our results suggest that age, mammographic density, genetic predictors of breast cancer, having more than two children, longer duration of breast‐feeding are significantly associated with increased risk of microcalcification clusters. However, most lifestyle risk factors for breast cancer seem to protect against presence of microcalcification clusters. More research is needed to study biological mechanisms behind microcalcifications formation. John Wiley & Sons, Inc. 2020-09-25 2021-03-01 /pmc/articles/PMC7821182/ /pubmed/32949149 http://dx.doi.org/10.1002/ijc.33302 Text en © 2020 The Authors. International Journal of Cancer published by John Wiley & Sons Ltd on behalf of Union for International Cancer Control. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Cancer Epidemiology
Azam, Shadi
Eriksson, Mikael
Sjölander, Arvid
Gabrielson, Marike
Hellgren, Roxanna
Czene, Kamila
Hall, Per
Predictors of mammographic microcalcifications
title Predictors of mammographic microcalcifications
title_full Predictors of mammographic microcalcifications
title_fullStr Predictors of mammographic microcalcifications
title_full_unstemmed Predictors of mammographic microcalcifications
title_short Predictors of mammographic microcalcifications
title_sort predictors of mammographic microcalcifications
topic Cancer Epidemiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7821182/
https://www.ncbi.nlm.nih.gov/pubmed/32949149
http://dx.doi.org/10.1002/ijc.33302
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