Risk prediction models for breast cancer integrating family history of breast cancer and prophylactic interventions
Abstract
Breast cancer risk assessment and prediction models are very important tools for the clinical management of healthy women carrying pathogenic variants in known cancer genes. We propose in this paper a comprehensive prediction model for the personalized clinical management of women with pathogenic variants in {\it BRCA1}. This model is able to estimate the risk of BC accounting for the full history of this cancer within the family, the personal and family history of risk-reducing salpingo-oophorectomy as well as the exact age of this intervention among family members. The effect of oophorectomy on the risk of developing breast cancer is evaluated by estimating and conducting inference about the regression coefficients in a Cox model with time-varying covariates. We model the within-family dependence in the ages at onset of cancer using a Gaussian copula whose correlation matrix accommodates the different pairwise family relationships. We develop an iterative algorithm to estimate the parameters of the considered model in the presence of a selection bias. We evaluated the performance of the proposed cancer risk estimation method from family data by simulations and illustrated its use through an application to the breast cancer family registry.
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BibTeX
@misc{seboka2026risk,
title = {{Risk prediction models for breast cancer integrating family history of breast cancer and prophylactic interventions}},
author = {Siriac Seboka and Laurent Briollais and Yun-Hee Choi and on behalf of BCFR and Lajmi Lakhal-Chaieb},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.30524},
eprint = {2609.30524},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.30524},
note = {CC0},
}
RIS
TY - UNPB
TI - Risk prediction models for breast cancer integrating family history of breast cancer and prophylactic interventions
AU - Siriac Seboka
AU - Laurent Briollais
AU - Yun-Hee Choi
AU - on behalf of BCFR
AU - Lajmi Lakhal-Chaieb
PY - 2026
DA - 2026/09/24
PB - arXiv
DO - 10.48550/arxiv.2609.30524
UR - https://arxiv.org/abs/2609.30524
AB - Breast cancer risk assessment and prediction models are very important tools for the clinical management of healthy women carrying pathogenic variants in known cancer genes. We propose in this paper a comprehensive prediction model for the personalized clinical management of women with pathogenic variants in {\it BRCA1}. This model is able to estimate the risk of BC accounting for the full history of this cancer within the family, the personal and family history of risk-reducing salpingo-oophorectomy as well as the exact age of this intervention among family members. The effect of oophorectomy on the risk of developing breast cancer is evaluated by estimating and conducting inference about the regression coefficients in a Cox model with time-varying covariates. We model the within-family dependence in the ages at onset of cancer using a Gaussian copula whose correlation matrix accommodates the different pairwise family relationships. We develop an iterative algorithm to estimate the parameters of the considered model in the presence of a selection bias. We evaluated the performance of the proposed cancer risk estimation method from family data by simulations and illustrated its use through an application to the breast cancer family registry.
ER -