Detecting Mammographically-Occult Cancer in Women with Dense Breasts

检测乳腺致密女性的乳房X线隐匿性癌症

• 批准号: 9296809
• 负责人: ROBERT M NISHIKAWA
• 金额: $ 20.23万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-03-03 至 2019-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9296809
• 关键词: Adipose tissue; Biological Markers; Biopsy; Breast; Clinical; Computers; Consult; Data Set; Development; Discriminant Analysis; Equilibrium; Eye; Financial cost; Future; Goals; Human; Image; Individual; Laws; Left; Letters; Magnetic Resonance Imaging; Malignant Neoplasms; Mammary Gland Parenchyma; Mammography; Maps; Notification; Physicians; ROC Curve; Radon; Research; Risk; Scheme; Specificity; Structure; Techniques; Testing; Tissues; Training; Ultrasonography; Validation; Woman; Women Physicians; base; breast density; cost; density; high risk; imaging biomarker; imaging modality; malignant breast neoplasm; novel; quantitative imaging; radiologist; screening

Most women in the USA who have dense breast tissue at screening mammography receive a letter notifying them that they have dense breasts and, therefore, mammography is less effective for them and could be associated with an increased risk of breast cancer. The letter advises women to talk with their physician whether they should have additional screening with ultrasound or magnetic resonance imaging (MRI). The benefit of additional screening is the possibility of detecting a mammographically occult cancer. However, the likelihood of detecting a cancer is not known, making it a difficult decision for the woman to balance the uncertain potential benefit against the known costs. These known costs are financial (as some states do not cover the supplemental screen) and the risk of an unnecessary biopsy, as the specificity of ultrasound and MRI are lower than mammography. The goal of our research is to develop imaging biomarkers for mammographically occult breast cancers on screening mammograms of women with dense breasts. This would allow women to know whether it is likely that they have a mammographically occult cancer that may be imaged with ultrasound or MRI. Our approach is to use a new and novel technique called a Radon Cumulative Distribution Transform (RadonCDT) to compare the structure of the left and right breasts. The RadonCDT is a non-linear technique the maps structures, which are created by the adipose and fibroglandular tissue, from the right breast to the left breast. Since the left and right breasts are generally symmetric, the presence of a mammographically occult cancer may produce subtle changes to the symmetry. These subtle differences are not visible to the human eye, but through the RadonCDT transform may become more apparent. We will develop the imaging biomarkers on a dataset of 150 mammographically occult cancer cases (clinical cases read as normal, but the woman has breast cancer detected on her next screening mammogram) and 150 normal cases (clinical cases read as normal and the woman does not have breast cancer detected on her next two screening mammograms). We will apply the RadonCDT to the images and then image features will be extracted from the transformed images. We anticipate that we will need less than 10 features. We will use a stepwise linear discriminant analysis to choose the best set of features from the full set of features extracted. We will use a linear discriminant classifier to merge the features so that the cases can be classified as containing a mammographically occult breast cancer or not. We will use a three-way cross validation to reduce bias. That is, we will divide the full dataset into three subsets, one for developing and selecting the features, one for training the classifier, and one for testing. Finally, we will use an independent dataset of 100 cases to validate the classifier. If we are successful, then up to 14 million women each year who have dense breasts will have needed information upon which to base her decision for getting supplemental screening.

在美国，大多数在筛查乳房X光检查时有致密乳腺组织的妇女都会收到一封通知信， 他们认为，他们有致密的乳房，因此，乳房X光检查对他们来说是不太有效的，可能是 与乳腺癌风险增加有关。这封信建议女性与她们的医生交谈 他们是否应该用超声波或磁共振成像（MRI）进行额外的筛查。的 额外筛查的好处是有可能检测到乳房X线检查的隐匿性癌症。但 检测到癌症的可能性是未知的，这使得妇女很难做出决定，以平衡 不确定的潜在利益与已知成本。这些已知成本是财务成本（因为有些州没有 覆盖补充屏幕）和不必要的活检的风险，因为超声和MRI的特异性 低于乳房X光检查。我们研究的目标是开发成像生物标志物， 乳房X光检查隐匿性乳腺癌对高密度乳房妇女的筛查。这 这将使妇女知道他们是否有可能患有乳房X光检查隐匿性癌症， 用超声波或核磁共振成像。我们的方法是使用一种新的和新颖的技术称为氡累积 分布变换（RadonCDT）来比较左右乳房的结构。RadonCDT是一种 非线性技术的地图结构，这是由脂肪和纤维腺体组织，从 右胸到左胸由于左右乳房通常是对称的， 乳房X光检查的隐匿性癌症可能会产生微妙的对称性变化。这些微妙的差异是 人眼不可见，但通过RadonCDT变换可能变得更加明显。我们将 在150个乳房X线摄影隐匿性癌症病例（临床病例）的数据集上开发成像生物标志物 阅读正常，但该妇女在下一次筛查乳房X光检查中发现乳腺癌）和150 正常病例（临床病例解读为正常，且该女性下次未检测到乳腺癌 两次筛查乳房X线照片）。我们将对图像应用RadonCDT，然后将图像特征 从变换后的图像中提取。我们预计，我们将需要不到10个功能。我们将使用一个 逐步线性判别分析，以从提取的全部特征集中选择最佳特征集。 我们将使用线性判别分类器来合并特征，以便可以将病例分类为 是否含有乳房X光检查发现的隐性乳腺癌我们将使用三向交叉验证来 减少偏差。也就是说，我们将完整的数据集分为三个子集，一个用于开发和选择 特征，一个用于训练分类器，一个用于测试。最后，我们将使用100个独立数据集 案例来验证分类器。如果我们成功了，那么每年将有多达1400万的妇女 乳房将需要的信息基础上，她的决定，以获得补充筛选。