Forskning
Udskriv Udskriv
Switch language
Region Hovedstaden - en del af Københavns Universitetshospital
Udgivet

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

Publikation: Bidrag til tidsskriftReviewForskningpeer review

DOI

  1. DBCG Kvalitetsdatabase for Brystkræft – resumé af årsrapport 2020

    Publikation: Bidrag til tidsskriftTidsskriftartikelFormidling

  2. In vivo Motion Correction in Super Resolution Imaging of Rat Kidneys

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  3. Dansk Kvalitetsdatabase for Mammografiscreening; resumé af rapport for sjette screeningsrunde

    Publikation: Bidrag til tidsskriftTidsskriftartikelFormidling

Vis graf over relationer

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.

OriginalsprogEngelsk
TidsskriftEuropean Journal of Radiology
Vol/bind127
Sider (fra-til)109019
ISSN0720-048X
DOI
StatusUdgivet - jun. 2020

Bibliografisk note

Copyright © 2020 Elsevier B.V. All rights reserved.

ID: 61289406