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

• 批准号: 10174885
• 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
• 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/10174885
• 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

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.

项目摘要 目前的乳腺癌筛查建议基本上是一刀切的方法， 因此，在有效性和资源利用率方面并不是最优的。这是因为典型的方法 重点是寻找患乳腺癌风险高于平均水平的女性亚群，以及 积极推广其他成像技术。然而，大多数(约70%)的女性 乳腺癌没有任何已知的危险因素。此外，大多数妇女(约88%) 永远不会得乳腺癌，而这些女性从乳腺癌筛查中受益最少。最大限度地提高 为了使所有女性受益，并将可能的伤害降至最低，调查人员主张使用 女性患乳腺癌的风险。要做到这一点，至关重要的是要有一个能够提供准确的 近期乳房X光检查-可检测乳腺癌(MBCA)风险识别极高或极低乳腺癌的女性 近期MBCA风险。此应用程序的目标是提供以个人为中心的MBCA风险标记，因此， 提供个性化的筛选策略。我们假设我们可以使用时间变化和横向变化 通过一种新的成像变换从顺序乳房X光显影中提取图像的差异 基于图像的风险标记物，可为女性提供准确的近期MBCA风险 乳房X光检查呈阴性。我们将建立一个数据库(N=1,200,400个病例和800个对照) (≥5岁)从40岁以上妇女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)用于验证。 我们将使用一种新的Radon累积分布变换(RCDT)来开发特定年份的风险标记， 卷积神经网络(CNN)和传统的非成像标记物(如年龄)。有效的RCDT 比较任何两个侧位和颞位的乳房X光照片，并突出两者之间的差异。 必须明确地对齐这两个图像。我们将使用CNN作为一个强大的成像标记来分析结果 乳房X光照片的RCDT图像。使用统计方法处理基于风险的纵向数据 集合，我们将结合基于成像的风险标记物和传统的非成像风险因素来开发两个 近期风险标记物，用于准确预测几年内患有MBCA的非常高的风险，以及 另一个原因是预测几年内罹患MBCA的风险非常低。高风险和低风险的标志将是 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。