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

基本信息

批准号：
8826571
负责人：
Bin Zheng
金额：
$ 22.68万
依托单位：
UNIVERSITY OF OKLAHOMA
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-04-01 至 2017-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8826571
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 Societies Staging Stratification Testing Time Tissues Uncertainty Woman Women&apos 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 screening success young woman

项目摘要

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).

描述（由申请人提供）：尽管乳腺癌是女性的主要癌症之一，但重复筛查人群中的乳腺癌检出率相当低（即每 1000 次检查中检出 3 至 5 种癌症）。早期发现乳腺癌的筛查从一开始就存在争议，但最近的事件强调需要通过识别在不久的将来（即五年内）患乳腺癌的风险高于平均水平的女性来制定和优化个体化筛查方案。年。建立最佳的个体化筛查方案，促进女性根据指定的风险组以不同的时间间隔和/或使用不同的成像方法进行筛查，不仅可以提高敏感性，从而检测出早期癌症，还可以降低总体成本和与癌症相关的焦虑。筛选计划。乳腺癌风险评估已研究多年；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，具有高辨别能力的乳腺癌风险模型将能够提高筛查范式的效率、功效和成本效益。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乳房之间双边乳房X线照相组织密度不对称的计算图像特征。同样重要的是，我们将使用在描述导致活检和/或癌症验证的高度可疑异常之前获得的乳房X光照片来开发和测试该预测器。为了实现我们的目标，我们将建立一个大型且多样化的全视野数字乳腺 X 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）在第一组病例后一到六年被证实患有癌症的阳性病例可用的阴性 FFDM 检查，(2) 筛查在感兴趣期间未回忆起的阴性病例，以及 (3) 由于可疑的乳房 X 光检查发现而回忆和/或活检病例，但后来证明是阴性或良性。计算出的双侧乳房X线照相组织不对称特征将用于开发新的风险预测器。除了评估整个数据库的整体分类性能之外，我们还将研究预测器结果的再现性以及预测器结果之间的关系分类性能以及相关阴性 FFDM 与由于实际检测到导致活检和/或确诊癌症的高度可疑发现而首次召回之间的时间滞后。我们还将评估其他几个常用风险因素（即年龄、家族史和乳腺密度 BIRADS）对预测器性能的影响（如果有）。 Bootstrapping 方法将用于计算预测器的性能水平和 95% 置信区间。通过将该风险预测器与其他现有风险模型相结合，我们将研究提高预测个体女性近期（<5 年）罹患乳腺癌风险的歧视力的可行性。

项目成果

期刊论文数量（22）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Reduction of false-positive recalls using a computerized mammographic image feature analysis scheme.

DOI：
10.1088/0031-9155/59/15/4357
发表时间：
2014-08-07
期刊：
Physics in medicine and biology
影响因子：
3.5
作者：
Tan M;Pu J;Zheng B
通讯作者：
Zheng B

Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm.

DOI：
10.1088/1361-6560/aaa1ca
发表时间：
2018-01-30
期刊：
Physics in medicine and biology
影响因子：
3.5
作者：
Heidari M;Khuzani AZ;Hollingsworth AB;Danala G;Mirniaharikandehei S;Qiu Y;Liu H;Zheng B
通讯作者：
Zheng B

Optimization of breast mass classification using sequential forward floating selection (SFFS) and a support vector machine (SVM) model.