Developing a personalized breast cancer screening tool using sequential mammograms

使用连续乳房X光检查开发个性化乳腺癌筛查工具

基本信息

批准号：
10410399
负责人：
Juhun Lee
金额：
$ 35.8万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-06-01 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10410399
关键词：
Advocate Age Age-Years Breast Cancer Detection Breast Cancer Risk Factor Breast Magnetic Resonance Imaging Collection Consensus Consultations Data Data Set Databases Development Diagnosis Effectiveness Frequencies Goals High Risk Woman Image Imaging Techniques Individual Lateral Magnetic Resonance Imaging Malignant Neoplasms Mammographic screening Mammography Medical Records Medical center Modeling Physicians Probability Protocols documentation Radon Recommendation Research Research Personnel Resources Risk Risk Factors Risk Marker Screening procedure Signal Transduction Specificity Subgroup System Universities Validation Woman base breast density cancer risk convolutional neural network data curation deep learning digital high risk imaging biomarker improved malignant breast neoplasm novel person centered personalized predictions personalized screening risk prediction screening screening guidelines

项目摘要

Project Summary The current breast cancer screening recommendations are essentially a one-size fits all approach and, therefore, not optimal in terms of effectiveness and resource utilization. This is because the typical approach focuses on finding subgroups of women who are at “higher than average risk” for developing breast cancer and aggressively promoting additional imaging techniques. However, most women (approximately 70%) who get breast cancer do not have any known risk factors. In addition, the majority of women (approximately 88%) never get breast cancer and these women benefit the least from breast cancer screening. To maximize the benefit to all women and minimize possible harms, investigators have advocated personalized screening using a woman's individual breast cancer risk. To do so, it is essential to have a marker that can provide an accurate near term mammography-detectable breast cancer (mBCa) risk to identify women with very high or very low near term mBCa risk. The goal of this application is to provide person-centered markers of mBCa risk, thus, offering a personalized screening strategy. We hypothesize that we can use temporal changes and lateral differences in images extracted by a novel imaging transformation from sequential mammograms to develop image-based risk markers that can provide women with an accurate near-term mBCa risk from their last negative mammography exam. We will build a database (N= 1,200, 400 cases and 800 controls) of sequential (≥ 5 years) full field digital mammograms collected from the medical records of women over 40 years of age for development and additional independent validation dataset (N = 600, 200 cases, 400 controls) for validation. We will develop year-specific risk markers using a novel Radon Cumulative Distribution Transform (RCDT), convolutional neural network (CNN), and traditional non-imaging markers (such as age). RCDT effectively compares any two lateral and temporal mammograms and highlights differences between the two without having to explicitly align the two images. We will use CNN as a robust imaging marker to analyze the resulting RCDT images from mammograms. Using a statistical approach for handling longitudinal data based on risk sets, we will combine imaging-based risk markers and conventional non-imaging risk factors to develop two near-term risk markers, one for accurately predicting very high risk of having mBCa within a few years and another for predicting very low risk of having mBCa within a few years. High-risk and low-risk markers will be optimized separately to maximize the sizes of accurately predicted high and low risk groups.

项目概要当前的乳腺癌筛查建议本质上是一种一刀切的方法，并且，因此，在有效性和资源利用率方面并不是最佳的，这是因为典型的方法。重点关注寻找“高于平均水平”患乳腺癌风险的女性亚群，以及然而，大多数女性（约 70%）都积极推广额外的成像技术。此外，大多数女性（约 88%）没有任何已知的乳腺癌风险因素。从未患过乳腺癌，这些女性从乳腺癌筛查中获益最少。为了使所有女性受益并尽量减少可能的危害，研究人员提倡使用个性化筛查为此，必须有一个可以提供准确信息的标记物。近期乳房 X 光检查可检测乳腺癌 (mBCa) 风险，以识别患有极高或极低的女性此应用程序的目标是提供以人为中心的 mBCa 风险标记，因此，我们勇敢地说，我们可以使用时间变化和横向变化。通过新颖的成像转换从连续乳房 X 线照片中提取的图像差异，以开发基于图像的风险标记，可以为女性提供最近一次准确的 mBCa 风险我们将建立一个序列数据库（N = 1,200，400 个病例和 800 个对照）。（≥ 5 年）从 40 岁以上女性的医疗记录中收集的全视野数字乳房 X 光照片开发和额外的独立验证数据集（N = 600、200 个病例、400 个对照）进行验证。我们将使用新颖的氡累积分布变换（RCDT）开发特定年份的风险标记，卷积神经网络（CNN）和传统的非影像标记（例如年龄）有效。比较任何两个横向和颞部乳房 X 光检查，并突出显示两者之间的差异，而无需我们将使用 CNN 作为强大的成像标记来分析结果。使用乳房 X 光检查的 RCDT 图像根据风险处理纵向数据。集，我们将结合基于影像的风险标记和传统的非影像风险因素来开发两种近期风险标记，用于准确预测几年内患有 mBCa 的极高风险，以及另一个用于预测几年内患有 mBCa 的风险非常低的标记是高风险和低风险标记。分别进行优化，以最大限度地扩大准确预测的高风险组和低风险组的规模。