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（由申请人提供）：尽管乳腺癌是女性的主要癌症之一，但在重复筛查人群中的乳腺癌检出率相当低（即每1000次检查中检出3至5例癌症）。早期乳腺癌筛查从一开始就存在争议，但最近的事件突出表明，需要通过识别在不久的将来（即五年内）患乳腺癌风险高于平均水平的女性，来制定和优化个性化筛查方案。建立最佳的个体化筛查方案，使妇女能够根据其指定的风险群体在不同的时间间隔和/或使用不同的成像方法进行筛查，这不仅可以提高敏感性，从而发现早期癌症，还可以降低与筛查项目相关的总体成本和焦虑。乳腺癌风险评估已经研究了很多年；然而，由于阳性预测值较低，目前还没有在常规临床实践中普遍接受的风险模型，特别是在筛查和诊断方面。毫无疑问，一个具有高度歧视性的乳腺癌风险模型将有助于提高筛查模式的效率、疗效和成本效益。我们建议开发和测试一种创新的风险预测器，该预测器主要基于代表左右双侧乳房乳房x线摄影组织密度不对称的计算机图像特征。同样重要的是，我们将在任何高度可疑的异常导致活检和/或癌症验证之前使用乳房x线照片开发和测试该预测器。为了实现我们的目标，我们将组装一个大型的、多样化的全视场数字乳房x线摄影（FFDM）图像数据库，其中包括顺序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)阳性病例，在第一次确诊后1至6年被证实患有癌症