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）：尽管是妇女的主要癌症之一，但重复筛查人群中的乳腺癌检测率很低（即每1000次检查的3至5个癌症）。从一开始就引起了筛查，筛查乳腺癌的早期发现一直存在争议，但是最近的事件强调了在不久的将来（即五年之内）鉴定出高于平均乳腺癌风险高于平均风险的女性，即开发和优化个性化筛查方案。建立最佳的个性化筛查方案，以不同的间隔和/或使用不同的成像方法筛查的女性根据其指定的风险组的筛查不仅会提高灵敏度，从而导致对早期癌症的检测，还可以减少与筛查计划相关的整体成本和焦虑。乳腺癌风险评估已有很多年了。但是，由于相当低的积极预测值，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有关。毫无疑问，具有高歧视能力的乳腺癌风险模型将使筛查范式的效率，功效和成本效益提高。我们建议开发和测试创新的风险预测变量，该预测指标主要基于代表左右乳房之间双边乳房X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X型组织密度不对称性的特征。重要的是，我们将使用获得高度可疑异常的任何乳房X线照片来开发和测试该预测因子，从而导致活检和/或癌症的验证。为了实现我们的目标，我们将通过顺序可用的图像和相关的临床信息组装一个大型多样的图像数据库（FFDM）图像。该数据库将包括三组病例，即（1）阳性病例已被证实，这些病例已在第一次验证后一到六年。 可用的负FFDM检查，（2）筛查在关注期间尚未召回的负面病例，以及（3）由于可疑的乳房X线摄影发现，召回和/或活检病例，但后来被证明为阴性或良性。计算出的双边乳腺X线X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总体分类性能外，我们还将研究预测器结果的可重复性以及预测变量之间的关系 分类性能和有关的负FFDM和第一次召回之间的时间滞后，这是由于实际发现高度可疑发现，导致活检和/或确认的癌症。我们还将评估其他几种常用的危险因素（即年龄，家族史和乳房密度Birads）的影响（如果有的话）。引导方法将用于计算预测器的性能水平和95％的置信区间。通过将此风险预测因子与其他现有风险模型合并，我们将 调查改善歧视能力在预测近期妇女患乳腺癌的风险方面的可行性（<5年）。 公共卫生相关性：该应用旨在基于（但不仅仅是）基于双边乳腺X线X线X线X摄影组织不对称的创新乳腺癌风险预测因子，如单个阴性乳房X线摄影检查所测量。我们的目标是确定在检查阴性后1至5年期间患乳腺癌的高风险和/或低风险的女性。这些信息可用于开发乳腺癌风险的高度歧视模型，然后可用于设计最佳的个性化筛查计划。