Threshold-Based Overlap of Breast Cancer High-Risk Classification Using Family History, Polygenic Risk Scores, and Traditional Risk Models in 180,398 Women
(2025) In Cancers 17(21).- Abstract
- Background: Breast cancer polygenic risk scores (PRS) and traditional risk models (e.g., the Gail model [Gail]) are known to contribute largely independent information, but it is unclear how the overlap varies by ancestry, age, disease type (invasive breast cancer, DCIS), and risk threshold. Methods: In a retrospective case–control study, we evaluated risk prediction performance in 180,398 women (161,849 of European ancestry; 18,549 of Asian ancestry). Odds ratios (ORs) from logistic regression models and the area under the receiver operating characteristic curve (AUC) were estimated. Results: PRS for invasive disease showed a stronger association in younger (
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/760c3c39-dff2-4f08-a14c-ad24e6ece044
- author
- Ho, P.J. ; Augustinsson, A. LU ; Jernström, H. LU and Li, J.
- author collaboration
- organization
- publishing date
- 2025
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- BRCA1, BRCA2, breast cancer, ductal carcinoma in situ (DCIS), Gail model, polygenic risk score (PRS), risk stratification, risk-based screening, adult, analysis, Article, breast cancer high risk classification, case control study, chi square test, classification, cohort analysis, controlled study, diagnostic test accuracy study, family history, female, gail model, genetic risk score, human, live birth, logistic regression analysis, major clinical study, middle aged, odds ratio, predictive model, pregnancy, receiver operating characteristic, retrospective study, risk assessment, risk factor, risk model, scoring system, sensitivity analysis, threshold based overlap, traditional risk model
- in
- Cancers
- volume
- 17
- issue
- 21
- article number
- 3561
- publisher
- MDPI AG
- external identifiers
-
- scopus:105025018397
- pmid:41228354
- ISSN
- 2072-6694
- DOI
- 10.3390/cancers17213561
- language
- English
- LU publication?
- yes
- id
- 760c3c39-dff2-4f08-a14c-ad24e6ece044
- date added to LUP
- 2026-03-31 15:46:31
- date last changed
- 2026-04-01 03:00:02
@article{760c3c39-dff2-4f08-a14c-ad24e6ece044,
abstract = {{Background: Breast cancer polygenic risk scores (PRS) and traditional risk models (e.g., the Gail model [Gail]) are known to contribute largely independent information, but it is unclear how the overlap varies by ancestry, age, disease type (invasive breast cancer, DCIS), and risk threshold. Methods: In a retrospective case–control study, we evaluated risk prediction performance in 180,398 women (161,849 of European ancestry; 18,549 of Asian ancestry). Odds ratios (ORs) from logistic regression models and the area under the receiver operating characteristic curve (AUC) were estimated. Results: PRS for invasive disease showed a stronger association in younger (}},
author = {{Ho, P.J. and Augustinsson, A. and Jernström, H. and Li, J.}},
issn = {{2072-6694}},
keywords = {{BRCA1; BRCA2; breast cancer; ductal carcinoma in situ (DCIS); Gail model; polygenic risk score (PRS); risk stratification; risk-based screening; adult; analysis; Article; breast cancer high risk classification; case control study; chi square test; classification; cohort analysis; controlled study; diagnostic test accuracy study; family history; female; gail model; genetic risk score; human; live birth; logistic regression analysis; major clinical study; middle aged; odds ratio; predictive model; pregnancy; receiver operating characteristic; retrospective study; risk assessment; risk factor; risk model; scoring system; sensitivity analysis; threshold based overlap; traditional risk model}},
language = {{eng}},
number = {{21}},
publisher = {{MDPI AG}},
series = {{Cancers}},
title = {{Threshold-Based Overlap of Breast Cancer High-Risk Classification Using Family History, Polygenic Risk Scores, and Traditional Risk Models in 180,398 Women}},
url = {{http://dx.doi.org/10.3390/cancers17213561}},
doi = {{10.3390/cancers17213561}},
volume = {{17}},
year = {{2025}},
}