Population-Targeted Risk Prediction to Guide Breast Cancer Screening in Cameroon

以人群为目标的风险预测指导喀麦隆的乳腺癌筛查

基本信息

批准号：
10331826
负责人：
Lauren Elizabeth Eyler Dang
金额：
$ 6.77万
依托单位：
UNIVERSITY OF CALIFORNIA BERKELEY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-01-01 至 2022-10-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10331826
关键词：
Address Affect Africa Africa South of the Sahara African African American Alcohol consumption Area Asia Asian Breast Breast Cancer Detection Breast Cancer Risk Factor Calibration California Cameroon Cancer Etiology Case-Control Studies Collaborations Communities Community Surveys Country Data Data Set Diagnosis Diagnostic Discrimination Disease Ensure Ethnic group Feedback Future Goals Grant High Risk Woman Hispanic Improve Access Incidence Interview Learning Lesion Logistics Los Angeles Malignant Neoplasms Measures Mentorship Methods Minority Groups Modeling National Cancer Institute Nigeria Nigerian Patients Performance Population Public Health ROC Curve Research Research Assistant Research Methodology Resource-limited setting Risk Risk Factors Sample Size Scheme Services Study models Subgroup Surgical Oncologist Survival Rate Target Populations Technical Expertise Testing Time Training Travel Uganda United States Universities Validation Weight Weight maintenance regimen Woman World Health Organization acceptability and feasibility base breast cancer registry cancer care cancer risk career career development case control design early screening ethnic diversity improved insight large datasets learning strategy machine learning method malignant breast neoplasm member mortality pilot test prediction algorithm programs research and development response risk prediction risk prediction model screening screening program

项目摘要

Project Summary/Abstract: As cancer becomes an increasingly important cause of mortality in sub-Saharan Africa, nations must improve their capacity to diagnose and treat the malignancies that most affect their populations. Breast cancer is the most common cancer and cause of cancer-related mortality for women in Cameroon, where two thirds of patients present with stage III or IV disease, and five-year survival rates are less than 30%. A longstanding collaboration between the Cameroonian Ministry of Public Health, the University of Buea, and the University of California, Los Angeles (the MBLA partnership) plans to conduct key stakeholder interviews in order to assess the feasibility and acceptability of implementing a breast cancer screening program that is initially offered to high-risk women. Risk-based screening aims to equitably target early screening efforts while ensuring that diagnostic and treatment capacity are sufficient to manage lesions identified through screening. Such a program requires a breast cancer risk prediction model that is applicable and acceptable to Cameroonian women and feasible to evaluate through a community-based screening program. Studies in the United States, Asia, and Nigeria demonstrate that breast cancer risk prediction models perform best when they are ethnic group-specific, but no breast cancer risk prediction model has been developed for Cameroonian women. The African Breast Cancer case-control Study (ABCS) contains breast cancer risk factor information for women from Nigeria, Uganda, and a small subset of women from Cameroon. Traditional approaches to risk prediction will likely suffer from small sample size in models trained on Cameroon data only or from bias in models trained and validated on the full, ethnically diverse dataset. In Aim 1, a subgroup-specific cross-validation method will be incorporated into the Super Learner ensemble prediction algorithm to develop a breast cancer risk prediction model that incorporates all ABCS data but is optimized for Cameroonian women. Aim 2 addresses MBLA members’ concerns that certain risk factors from ABCS will be difficult to evaluate by community survey. Targeted learning methods will be used to define metrics for comparing risk prediction models including and excluding these variables so that Cameroonian stakeholders can evaluate whether to include these risk factors in their breast cancer risk prediction model. In Aim 3, an R shiny app will be developed, tested, and optimized in order to facilitate use of a Cameroon-specific breast cancer risk model in a future screening program. The methods developed and tested in this project could help to optimize cancer risk prediction models for other ethnic groups with limited data in sub-Saharan Africa and globally. This research will be conducted under the mentorship of the MBLA collaboration, UC Berkeley’s leaders in the field of targeted learning, and the UCSF Global Cancer Program. By providing protected time for training, research, and career development, this grant will facilitate the applicant’s progress towards becoming a breast surgical oncologist researching methods of improving access to cancer care in Africa.

项目摘要/摘要：随着癌症成为撒哈拉以南非洲死亡率的日益重要的原因，各国必须改善他们诊断和治疗最大的恶性肿瘤的能力。乳腺癌是喀麦隆最常见的癌症和与癌症相关的癌症相关死亡率，其中三分之二的三分之二患有III期或IV期疾病的患者，五年生存率小于30％。长期存在喀麦隆公共卫生部，布亚大学和大学之间的合作加利福尼亚，洛杉矶（MBLA合作伙伴关系）计划进行关键的利益相关者面试，以评估实施最初提供的乳腺癌筛查计划的可行性和可接受性高风险女性。基于风险的筛查旨在同样针对早期筛查工作，同时确保诊断和治疗能力足以管理通过筛查确定的病变。这样的计划需要适用且可接受的乳腺癌风险预测模型妇女可行，可以通过基于社区的筛查计划进行评估。美国的研究，亚洲和尼日利亚证明乳腺癌风险预测模型在族裔时表现最好喀麦隆妇女还没有开发针对群体特异性的，但尚未开发乳腺癌风险预测模型。这非洲乳腺癌病例对照研究（ABC）包含女性乳腺癌风险因素信息来自尼日利亚，乌干达和喀麦隆的一小部分妇女。传统的风险预测方法仅在喀麦隆数据训练的模型中，可能会遭受小样本量或模型中的偏差。在完整的种族多样性数据集中经过培训和验证。在AIM 1中，亚组特异性交叉验证方法将纳入超级学习者集合预测算法以发展乳腺癌风险预测模型包含所有ABC数据但针对喀麦隆妇女进行了优化。目标2 解决了MBLA成员的担忧，即ABC的某些风险因素将难以评估社区调查。有针对性的学习方法将用于定义比较风险预测的指标包括和排除这些变量在内的模型，以便喀麦隆的利益相关者可以评估是否要将这些危险因素包括在其乳腺癌风险预测模型中。在AIM 3中，r闪亮的应用程序将是开发，测试和优化，以促进喀麦隆特异性乳腺癌风险模型未来筛选计划。在该项目中开发和测试的方法可能有助于优化癌症风险在撒哈拉以南非洲和全球范围内数据有限的其他族裔的预测模型。这项研究将在MBLA合作的心态下进行，加州大学伯克利分校的领导人有针对性的学习和UCSF全球癌症计划。通过提供受保护的培训时间，研究的时间和职业发展，这笔赠款将促进申请人成为乳房外科手术的进步肿瘤学家研究改善非洲获得癌症护理的方法。