Impact of adding breast density to breast cancer risk models: A systematic review

Research output: Contribution to journalReviewResearchpeer-review

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Impact of adding breast density to breast cancer risk models : A systematic review. / Vilmun, Bolette Mikela; Vejborg, Ilse; Lynge, Elsebeth; Lillholm, Martin; Nielsen, Mads; Nielsen, Michael Bachmann; Carlsen, Jonathan Frederik.

In: European Journal of Radiology, Vol. 127, 109019, 06.2020.

Research output: Contribution to journalReviewResearchpeer-review

Harvard

Vilmun, BM, Vejborg, I, Lynge, E, Lillholm, M, Nielsen, M, Nielsen, MB & Carlsen, JF 2020, 'Impact of adding breast density to breast cancer risk models: A systematic review', European Journal of Radiology, vol. 127, 109019. https://doi.org/10.1016/j.ejrad.2020.109019

APA

Vilmun, B. M., Vejborg, I., Lynge, E., Lillholm, M., Nielsen, M., Nielsen, M. B., & Carlsen, J. F. (2020). Impact of adding breast density to breast cancer risk models: A systematic review. European Journal of Radiology, 127, [109019]. https://doi.org/10.1016/j.ejrad.2020.109019

Vancouver

Vilmun BM, Vejborg I, Lynge E, Lillholm M, Nielsen M, Nielsen MB et al. Impact of adding breast density to breast cancer risk models: A systematic review. European Journal of Radiology. 2020 Jun;127. 109019. https://doi.org/10.1016/j.ejrad.2020.109019

Author

Vilmun, Bolette Mikela ; Vejborg, Ilse ; Lynge, Elsebeth ; Lillholm, Martin ; Nielsen, Mads ; Nielsen, Michael Bachmann ; Carlsen, Jonathan Frederik. / Impact of adding breast density to breast cancer risk models : A systematic review. In: European Journal of Radiology. 2020 ; Vol. 127.

Bibtex

@article{9b11f635727844afa0cc15bbb3a18249,
title = "Impact of adding breast density to breast cancer risk models: A systematic review",
abstract = "Purpose: Assessment of a woman's risk of breast cancer is essential when moving towards personalized screening. Breast density is a well-known risk factor and has the potential to improve accuracy of risk prediction models. In this study we reviewed the impact on model performance of adding breast density to clinical breast cancer risk prediction models. Methods: We conducted a systematic review using a pre-specified search strategy for PubMed, EMBASE, Web of Science, and Cochrane Library from January 2007 until November 2019. Studies were screened using the Covidence software. Eligible studies developed or modified existing breast cancer risk prediction models applicable to the general population of women by adding breast density to the model. Improvement in discriminatory accuracy was measured as an increase in the Area Under the Curve or concordance statistics. Results: Eleven eligible studies were identified by the search and one by reference check. Four studies modified the Gail model, four modified the Tyrer-Cuzick model, and five studies developed new models. Several methods were used to measure breast density, including visual, semi- and fully automated methods. Eleven studies reported discriminatory accuracy and one study reported calibration. Seven studies found a statistically significantly increased discriminatory accuracy when including density in the model. The increase in AUC ranged 0.03 to 0.14. Four studies did not report on statistical significance, but reported an increased AUC ranging from 0.01 to 0.06. Conclusion: Including mammographic breast density has the potential to improve breast cancer risk prediction models. However, all models demonstrated limited discrimination accuracy.",
keywords = "Breast cancer screening, Breast density, Mammography, Risk assessment, Risk prediction models, Systematic review",
author = "Vilmun, {Bolette Mikela} and Ilse Vejborg and Elsebeth Lynge and Martin Lillholm and Mads Nielsen and Nielsen, {Michael Bachmann} and Carlsen, {Jonathan Frederik}",
year = "2020",
month = "6",
doi = "10.1016/j.ejrad.2020.109019",
language = "English",
volume = "127",
journal = "European Journal of Radiology",
issn = "0720-048X",
publisher = "Elsevier Ireland Ltd",

}

RIS

TY - JOUR

T1 - Impact of adding breast density to breast cancer risk models

T2 - A systematic review

AU - Vilmun, Bolette Mikela

AU - Vejborg, Ilse

AU - Lynge, Elsebeth

AU - Lillholm, Martin

AU - Nielsen, Mads

AU - Nielsen, Michael Bachmann

AU - Carlsen, Jonathan Frederik

PY - 2020/6

Y1 - 2020/6

N2 - Purpose: Assessment of a woman's risk of breast cancer is essential when moving towards personalized screening. Breast density is a well-known risk factor and has the potential to improve accuracy of risk prediction models. In this study we reviewed the impact on model performance of adding breast density to clinical breast cancer risk prediction models. Methods: We conducted a systematic review using a pre-specified search strategy for PubMed, EMBASE, Web of Science, and Cochrane Library from January 2007 until November 2019. Studies were screened using the Covidence software. Eligible studies developed or modified existing breast cancer risk prediction models applicable to the general population of women by adding breast density to the model. Improvement in discriminatory accuracy was measured as an increase in the Area Under the Curve or concordance statistics. Results: Eleven eligible studies were identified by the search and one by reference check. Four studies modified the Gail model, four modified the Tyrer-Cuzick model, and five studies developed new models. Several methods were used to measure breast density, including visual, semi- and fully automated methods. Eleven studies reported discriminatory accuracy and one study reported calibration. Seven studies found a statistically significantly increased discriminatory accuracy when including density in the model. The increase in AUC ranged 0.03 to 0.14. Four studies did not report on statistical significance, but reported an increased AUC ranging from 0.01 to 0.06. Conclusion: Including mammographic breast density has the potential to improve breast cancer risk prediction models. However, all models demonstrated limited discrimination accuracy.

AB - Purpose: Assessment of a woman's risk of breast cancer is essential when moving towards personalized screening. Breast density is a well-known risk factor and has the potential to improve accuracy of risk prediction models. In this study we reviewed the impact on model performance of adding breast density to clinical breast cancer risk prediction models. Methods: We conducted a systematic review using a pre-specified search strategy for PubMed, EMBASE, Web of Science, and Cochrane Library from January 2007 until November 2019. Studies were screened using the Covidence software. Eligible studies developed or modified existing breast cancer risk prediction models applicable to the general population of women by adding breast density to the model. Improvement in discriminatory accuracy was measured as an increase in the Area Under the Curve or concordance statistics. Results: Eleven eligible studies were identified by the search and one by reference check. Four studies modified the Gail model, four modified the Tyrer-Cuzick model, and five studies developed new models. Several methods were used to measure breast density, including visual, semi- and fully automated methods. Eleven studies reported discriminatory accuracy and one study reported calibration. Seven studies found a statistically significantly increased discriminatory accuracy when including density in the model. The increase in AUC ranged 0.03 to 0.14. Four studies did not report on statistical significance, but reported an increased AUC ranging from 0.01 to 0.06. Conclusion: Including mammographic breast density has the potential to improve breast cancer risk prediction models. However, all models demonstrated limited discrimination accuracy.

KW - Breast cancer screening

KW - Breast density

KW - Mammography

KW - Risk assessment

KW - Risk prediction models

KW - Systematic review

U2 - 10.1016/j.ejrad.2020.109019

DO - 10.1016/j.ejrad.2020.109019

M3 - Review

C2 - 32361308

AN - SCOPUS:85083874297

VL - 127

JO - European Journal of Radiology

JF - European Journal of Radiology

SN - 0720-048X

M1 - 109019

ER -

ID: 242409459