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）：尽管是女性中的主要癌症之一，但重复筛查人群中的乳腺癌检出率相当低（即，每1000次检查发现3至5例癌症）。早期发现乳腺癌的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）发生乳腺癌的风险高于平均水平的妇女，来制定和优化个性化的筛查方案。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，这不仅会提高灵敏度，从而发现早期癌症，而且还可以降低总体成本和与筛查计划相关的焦虑。乳腺癌风险评估已研究多年;然而，由于阳性预测值相当低，因此在常规临床实践中没有普遍接受的现有风险模型，特别是与筛查和诊断相关的风险模型。毫无疑问，具有高区分能力的乳腺癌风险模型将能够提高筛查模式的效率、功效和成本效益。我们建议开发和测试一个创新的风险预测，主要是基于计算的图像特征，代表双边乳房摄影组织密度左右乳房之间的不对称性。同样重要的是，我们将使用在任何高度可疑异常导致活检和/或癌症验证之前获得的乳房X线照片来开发和测试该预测因子。为了实现我们的目标，我们将收集一个大型的、多样化的全视野数字乳腺X射线摄影（FFDM）图像数据库，其中包含连续可用的图像和相关临床信息。该数据库将包括三组病例，即（1）在首次确诊后1至6年被证实患有癌症的阳性病例， 可用的阴性FFDM检查，（2）筛选在关注期内未召回的阴性病例，和（3）由于可疑乳腺X线摄影结果召回和/或活检病例，但后来证明为阴性或良性。计算的双侧乳房X线摄影组织不对称特征将用于开发新的风险预测因子。除了评估整个数据库的整体分类性能外，我们还将研究预测结果的可重复性以及预测结果与预测结果之间的关系。 分类性能和所讨论的阴性FFDM与由于实际检测到高度可疑的结果导致活检和/或确诊癌症而导致的首次召回之间的时间滞后。我们还将评估其他几种常用风险因素（即，年龄、家族史和乳腺密度BIRADS）对预测因子的影响。将使用自举方法计算预测器的性能水平和95%置信区间。通过将此风险预测器与其他现有风险模型相结合，我们将 调查提高预测个别妇女近期（<5年）患乳腺癌风险的辨别能力的可行性。 公共卫生关系：该应用旨在开发和测试一种创新的乳腺癌风险预测因子，该预测因子主要（但不仅限于）基于从单次阴性乳腺X线摄影检查中测量的双侧乳腺X线摄影组织不对称性。我们的目标是在阴性检查后1至5年内识别出患有乳腺癌的高风险和/或低风险女性。这些信息可以用于开发一个高度区分乳腺癌风险的模型，然后用于设计最佳的个性化筛查计划。