整合乳腺癌家族史与预防性干预的乳腺癌风险预测模型
Risk prediction models for breast cancer integrating family history of breast cancer and prophylactic interventions
- Université Laval（拉瓦尔大学）
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital（圣迈克尔医院伦内菲尔德-塔纳鲍姆研究所）
- Biostatistics division, Dalla Lana School of Public Health, University of Toronto（多伦多大学达拉·兰纳公共卫生学院生物统计系）
- Department of Epidemiology and Biostatistics, Western University（韦仕敦大学流行病学与生物统计学系）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一个整合家族史与预防性干预的BRCA1致病性变异携带者乳腺癌风险预测模型，采用含时变协变量的Cox模型和高斯copula，并通过模拟与真实数据验证其性能。
AI中文摘要：
乳腺癌风险评估与预测模型是临床管理携带已知癌症基因致病性变异健康女性的重要工具。本文提出一个针对携带BRCA1致病性变异女性的个性化临床管理综合预测模型。该模型能够估计乳腺癌风险，同时考虑家族内该癌症的完整病史、个人及家族中降低风险的输卵管卵巢切除术史，以及家族成员进行该干预的确切年龄。通过估计含时变协变量的Cox模型中的回归系数并进行推断，评估卵巢切除术对乳腺癌发生风险的影响。我们使用高斯copula对家族内癌症发病年龄的相关性进行建模，其相关矩阵适应不同的成对家族关系。我们开发了一种迭代算法，在选择偏倚存在的情况下估计所考虑模型的参数。我们通过模拟评估了所提出的基于家族数据的癌症风险估计方法的性能，并通过应用于乳腺癌家族登记处说明了其用途。
英文摘要：
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.
