Mammographic Density and Tissue Asymmetry Based Breast Cancer Risk Stratification

基于乳房 X 光密度和组织不对称性的乳腺癌风险分层

• 批准号: 8282037
• 负责人: Bin Zheng
• 金额: $ 23.07万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-04-01 至 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8282037
• 关键词: Age; Benign; Bilateral; Biological Process; Biopsy; Breast; Breast Cancer Detection; Breast Cancer Early Detection; Cancer Detection; Classification; Clinical; Confidence Intervals; Data Analyses; Data Set; Databases; Dependency; Detection; Diagnosis; Diagnostic; Digital Mammography; Early Diagnosis; Environment; Event; Family; Frequencies; Future; Genetic Programming; High Risk Woman; Image; Individual; Laboratory Research; Lead; Left; Lesion; Machine Learning; Malignant Neoplasms; Mammary Gland Parenchyma; Mammographic Density; Mammography; Measures; Medical; Methods; Modeling; Outcome; Patients; Pattern; Performance; Phenotype; Population; Positioning Attribute; Predictive Value; Recording of previous events; Regimen; Reproducibility; Risk; Risk Assessment; Risk Factors; Scheme; Screening for cancer; Screening procedure; Societies; Staging; Stratification; Testing; Time; Tissues; Uncertainty; Woman; Women' s Group; Work; base; breast density; cancer risk; clinical practice; computerized; cost; cost effectiveness; density; design; high risk; imaging modality; improved; innovation; interest; malignant breast neoplasm; mortality; programs; radiologist; success; young woman

DESCRIPTION (provided by applicant): Despite being one of the leading cancers in women, breast cancer detection rates in a repeat screened population are quite low (i.e., 3 to 5 cancers detected per 1000 examinations). Screening for the early detection of breast cancer has been controversial from the start, but recent events highlight the need to develop and optimize individualized screening regimens by identifying women who are at higher than average risk of developing breast cancer in the near future, namely within five years. Establishing optimal individualized screening regimens that facilitate women to be screened at different intervals and/or with different imaging methods based on their assigned risk group will not only increase sensitivity, resulting in the detection of earlier cancers, but also reduce overall cost and anxiet associated with screening programs. Breast cancer risk assessment has been studied for many years; however, due to reasonably low positive predictive values there are no existing risk models that are universally accepted in routine clinical practice, in particular as related to screening and diagnosis. There is no doubt that a breast cancer risk model with high discriminatory power will enable an increase in efficiency, efficacy, and cost effectiveness of screening paradigms. We propose to develop and test an innovative risk predictor that is based primarily on computed image features representing bilateral mammographic tissue density asymmetry between left and right breasts. As important, we will develop and test this predictor using mammograms acquired prior to any depiction of a highly suspicious abnormality leading to a biopsy and/or a verification of cancer. To achieve our objectives we will assemble a large and diverse image database of full-field digital mammography (FFDM) images with sequentially available images and related clinical information. The database will include three groups of cases, namely (1) positive cases that were verified to have cancer one to six years after the first available negative FFDM examination, (2) screening negative cases that have not been recalled during the period of interest, and (3) recalled and/or biopsied cases due to suspicious mammographic findings, but later proven to be negative or benign. Computed bilateral mammographic tissue asymmetry features will be used to develop the new risk predictor. In addition to evaluating the overall classification performance on the entire database, we will investigate the reproducibility of the predictor's results and the relationship between predictor's classification performance and the time lag between a negative FFDM in question and the first recall due to the actual detection of a highly suspicious finding leading to a biopsy and/or a confirmed cancer. We will also assess the impact, if any, of several other commonly used risk factors (i.e., age, family history, and breast density BIRADS) on predictor's performance. A bootstrapping method will be used to compute predictor's performance levels and 95% confidence intervals. By incorporating this risk predictor with other existing risk models, we will investigate the feasibility of improving discriminatory power in predicting risk of individual women developing breast cancer in near-term (<5 years). PUBLIC HEALTH RELEVANCE: This application aims to develop and test an innovative breast cancer risk predictor based primarily (but not solely) on bilateral mammographic tissue asymmetry as measured from a single negative mammography examination. We aim to identify women who are at high and/or low risk of developing breast cancer during the time period of 1 to 5 years following a negative examination. This information could be used for developing a highly discriminative model of the breast-cancer risk that could be then used for designing optimal individualized screening plans.

描述(申请人提供)：尽管乳腺癌是女性的主要癌症之一，但在重复筛查的人群中，乳腺癌的发现率相当低(即每1000次检查中发现3至5例癌症)。从一开始，乳腺癌早期检测的筛查就一直存在争议，但最近的事件突显了开发和优化个体化筛查方案的必要性，方法是识别在不久的将来，即在五年内患乳腺癌的风险高于平均水平的妇女。建立最佳的个体化筛查方案，便于妇女根据其指定的风险组在不同的时间间隔和/或使用不同的成像方法进行筛查，不仅可以提高敏感性，从而发现早期癌症，而且还可以降低与筛查计划相关的总体成本和焦虑。乳腺癌风险评估已经研究多年；然而，由于阳性预测值相当低，目前还没有在常规临床实践中被普遍接受的风险模型，特别是与筛查和诊断相关的风险模型。毫无疑问，具有高分辨能力的乳腺癌风险模型将使筛查范例的效率、有效性和成本效益得到提高。我们建议开发和测试一种创新的风险预测指标，它主要基于计算机图像特征，代表左右乳房之间的双侧乳房X光摄影组织密度不对称。同样重要的是，我们将使用在描述高度可疑的异常导致活组织检查和/或癌症验证之前获得的乳房X光照片来开发和测试这一预测指标。为了实现我们的目标，我们将汇集一个大型和多样化的全场数字乳房X光摄影(FFDM)图像数据库，其中包括连续可用的图像和相关的临床信息。该数据库将包括三组病例，即(1)第一组病例后一至六年被证实患有癌症的阳性病例 现有的阴性FFDM检查，(2)筛查在感兴趣期间未被召回的阴性病例，以及(3)因可疑的乳房X光检查结果被召回和/或活组织检查，但后来被证明是阴性或良性的病例。计算的双侧乳腺X光摄影组织不对称特征将被用于开发新的风险预测指标。除了评估整个数据库的整体分类性能外，我们还将调查预测者结果的重复性以及预测者之间的关系 分类性能以及由于实际检测到导致活组织检查和/或确诊癌症的高度可疑发现而导致的阴性FFDM值与第一次召回之间的时间间隔。我们还将评估其他几个常用的危险因素(即年龄、家族史和乳房密度BIRADS)对预测者表现的影响。将使用自举方法来计算预测器的性能水平和95%的可信区间。通过将此风险预测器与其他现有风险模型相结合，我们将 研究在预测个体女性在近期(5年)罹患乳腺癌的风险方面提高判别力的可行性。 公共卫生相关性：这项应用旨在开发和测试一种创新的乳腺癌风险预测指标，主要(但不仅仅是)基于从单一阴性乳房X光检查中测量的双侧乳房X光检查组织的不对称性。我们的目标是确定在阴性检查后的1至5年内患乳腺癌的高风险和/或低风险的妇女。这些信息可以用来开发一个高度区分乳腺癌风险的模型，然后可以用来设计最佳的个性化筛查计划。