Dynamic prediction incorporating time-varying covariates for the onset of breast cancer

结合时变协变量的乳腺癌发病动态预测

• 批准号: 10709203
• 负责人: Shu Jiang
• 金额: $ 21.02万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10709203
• 关键词: Affect; Behavioral; Breast Cancer Risk Factor; Breast Feeding; Classification; Code; Early Diagnosis; Epidemiology; Ethnic Population; Future; Grain; Health; Image; Incidence; Intake; Mammography; Outcome; Pathology; Pathway interactions; Performance; Population; Prevention strategy; Recording of previous events; Risk; Risk Factors; Risk Reduction; Statistical Methods; Stratification; Time; Update; Variant; Vegetables; Woman; black women; breast cancer diagnosis; cohort; digital; follow-up; improved; insight; malignant breast neoplasm; novel; racial population; risk prediction; targeted treatment; triple-negative invasive breast carcinoma

PROJECT SUMMARY Triple negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited treatment options and poor survival. Approximately 12% to 17% of women with breast cancer are diagnosed with TNBC. Women with TNBC have relatively poor outcomes and cannot be treated with targeted therapies. However, the risk of TNBC is not uniform across all race-ethnic groups of the US population. Review of epidemiologic risk factors and TNBC incidence shows limited insight to variation in risk or risk reduction with the exception of history of breast feeding and higher vegetable and grain intake. We aim to bring personalized dynamic prediction to improve the current TNBC risk classification paradigm to make full use of the longitudinal information, in addition to the baseline information, where risk prediction/stratification can be updated as new observations are gathered to reflect the woman’s latest health- and behavioral-related status. Specifically, we aim to investigate 5- and 10-year TNBC risk prediction performance by proposing novel statistical methods that fully utilize the personalized mammogram-based risk factors from repeated mammogram images. The proposed study capitalizes on the WashU TNBC cohort with rich digital mammograms with well-studied BC risk factors, 10 years of follow-up and pathology confirmed incident TNBC. All proposed statistical methods will be supplemented by R code that we will make publicly available.

项目摘要 三重阴性乳腺癌（TNBC）是乳腺癌的积极亚型，有限 治疗选择和生存不佳。大约12％至17％的乳腺癌女性是 被诊断为TNBC。 TNBC的妇女的结果相对较差，无法治疗 有针对性的疗法。但是，在所有种族群体中，TNBC的风险并不统一 美国人口。流行病学风险因素和TNBC事件的审查显示有限的见解 除母乳病史外，风险或风险降低和降低风险变化 蔬菜和谷物摄入量。我们旨在带来个性化的动态预测，以改善 当前的TNBC风险分类范式充分利用纵向信息， 除了基线信息，可以更新风险预测/分层作为新信息 收集观察结果是为了反映该女子的最新健康和行为相关的身份。 具体而言，我们旨在通过提案调查5年和10年的TNBC风险预测绩效 新的统计方法完全利用了基于个性化乳3的个性化风险因素 重复的乳房X线照片图像。拟议的研究将带有Washu TNBC队列的研究 丰富的数字乳房X线照片具有良好的BC风险因素，随访和病理10年 确认事件TNBC。所有提出的统计方法将以R代码补充 将公开可用。

项目成果

Association and Prediction Utilizing Craniocaudal and Mediolateral Oblique View Digital Mammography and Long-Term Breast Cancer Risk.

• DOI: 10.1158/1940-6207.capr-22-0499
• 发表时间: 2023-09-01
• 期刊: CANCER PREVENTION RESEARCH
• 影响因子: 3.3
• 作者: Chen, Simin;Tamimi, Rulla M.;Colditz, Graham A.;Jiang, Shu
• 通讯作者: Jiang, Shu

Longitudinal Analysis of Change in Mammographic Density in Each Breast and Its Association With Breast Cancer Risk.

• DOI: 10.1001/jamaoncol.2023.0434
• 发表时间: 2023-06-01
• 期刊: JAMA oncology
• 影响因子: 28.4
• 作者: Jiang S;Bennett DL;Rosner BA;Colditz GA
• 通讯作者: Colditz GA

Modeling correlated pairs of mammogram images.

对相关的乳房 X 光图像对进行建模。

• DOI: 10.1002/sim.10002
• 发表时间: 2024
• 期刊: Statistics in medicine
• 影响因子: 2
• 作者: Jiang,Shu;Colditz,GrahamA
• 通讯作者: Colditz,GrahamA

Repeated measures of mammographic density and texture to evaluate prediction and risk of breast cancer: a systematic review of the methods used in the literature.

• DOI: 10.1007/s10552-023-01739-2
• 发表时间: 2023-11
• 期刊: CANCER CAUSES & CONTROL
• 影响因子: 2.3
• 作者: Anandarajah, Akila;Chen, Yongzhen;Stoll, Carolyn;Hardi, Angela;Jiang, Shu;Colditz, Graham A.
• 通讯作者: Colditz, Graham A.

Identifying regions of interest in mammogram images.

• DOI: 10.1177/09622802231160551
• 发表时间: 2023-05
• 期刊: STATISTICAL METHODS IN MEDICAL RESEARCH
• 影响因子: 2.3
• 作者: Jiang, Shu;Cao, Jiguo;Colditz, Graham A. A.
• 通讯作者: Colditz, Graham A. A.