Detecting Mammographically-Occult Cancer in Women with Dense Breasts Using Digital Breast Tomosynthesis

使用数字乳房断层合成技术检测乳房致密女性的乳房X线隐匿性癌症

• 批准号: 10580985
• 负责人: Juhun Lee
• 金额: $ 39.25万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-12-08 至 2027-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10580985
• 关键词: 3-Dimensional; Area; Biopsy; Breast; Breast Cancer Detection; Breast Cancer Risk Factor; Breast Magnetic Resonance Imaging; Cancer Detection; Clinical; Consult; Data Set; Development; Digital Breast Tomosynthesis; Digital Mammography; Financial cost; Future; Goals; Image; Imaging Techniques; Individual; Lateral; Laws; Lead; Left; Letters; Magnetic Resonance Imaging; Malignant Neoplasms; Mammary Gland Parenchyma; Mammographic screening; Mammography; Methods; Modality; Modeling; Notification; Physicians; Process; ROC Curve; Radon; Recommendation; Research; Research Personnel; Risk; Scanning; Sensitivity and Specificity; Signal Transduction; Slice; Specificity; Target Populations; Techniques; Testing; Thick; Three-Dimensional Imaging; Tissues; Training; Translating; Validation; Woman; artificial intelligence method; base; biomarker validation; breast density; breast imaging; convolutional neural network; cost; digital; high risk; imaging biomarker; imaging facilities; malignant breast neoplasm; novel; screening; supplemental screening; tomosynthesis; ultrasound

Most women in the USA who have dense breasts at screening mammography receive a letter notifying them that mammography is less effective for them and having dense breasts increases the 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 possible benefit of additional screening is detecting a mammographically occult (MO) cancer. However, the likelihood that a woman has a missed cancer is not known. Thus, women are left with a difficult decision, balancing the uncertain potential benefit of additional screening 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. We have developed a novel technique using a Radon Cumulative Distribution Transform (RCDT) to detect MO cancers. The RCDT can highlight subtle suspicious signals by detecting asymmetries between the left and right mammograms. Our technique achieved an area under the ROC curve of 0.81 using screening mammograms. Digital breast tomosynthesis (DBT), a pseudo-3D imaging technique, is replacing mammography in the USA, because of its higher sensitivity and specificity. However, MO cancers still exist in DBT. The goal of our research is to develop imaging biomarkers for MO cancers on screening DBT of women with dense breasts. This would allow women to know the likelihood that they have an MO cancer and, thereby, allow them to make a more informed choice regarding supplemental screening. The key difference between DBT and standard 2D mammography is the available information in the z-direction. Such additional information provides advantages for cancer detection, but it also adds technical complexity when applying RCDT on DBT images. There are three ways to process DBT exams for RCDT: 1) applying RCDT on 2D DBT slices, 2) applying RCDT on synthetic mammograms from DBT, and 3) applying the 3D RCDT on DBT volumes. To develop imaging biomarkers for MO cancer in screening DBT, we need to investigate the optimal method to process DBT for RCDT. We will develop imaging biomarkers for the three methods using a developmental dataset of 900 MO cancer cases (clinical cases read as normal, but the woman has breast cancer detected on her next screening DBT) and 1800 cases (clinical cases read as normal and the woman does not have breast cancer detected on her next two screening DBTs). We will utilize a 2D convolutional neural network (CNN) and a 3D CNN as robust classifiers to analyze the RCDT processed DBT for MO cancer detection. Using a 5-fold cross-validation, we will train CNNs for each method and find the optimal method to process DBT for MO cancer detection. Finally, we will use an independent dataset of 100 cases to validate the classifier. If we are successful, then up to 15 million women each year who have dense breasts will have needed information upon which to base their decision for getting supplemental screening.

美国大多数在筛查乳房X线摄影时乳房茂密的女性收到通知她们的信 乳房X线摄影对他们有效，乳房致密会增加患乳腺癌的风险。 这封信建议妇女与医生交谈是否应该与 超声或磁共振成像（MRI）。额外筛选的可能好处是检测 乳房X线神秘性（MO）癌症。但是，女人患癌症的可能性不是 已知。因此，妇女有一个艰难的决定，平衡了不确定的潜在利益 筛选已知成本。这些已知成本是财务（因为某些州不涵盖 补充屏幕）和不必要的活检的风险，因为超声和MRI的特异性较低 比乳房X线摄影。我们已经使用ra累积分布变换开发了一种新型技术 （RCDT）检测mo癌。 RCDT可以通过检测不对称来突出显示微妙的可疑信号 在左右乳房X线照片之间。我们的技术使用ROC曲线在0.81的ROC曲线下实现了一个区域 筛选乳房X线照片。数字乳房间压合成（DBT）是一种伪3D成像技术，正在替换 由于其更高的敏感性和特异性，在美国乳房X线摄影。但是，莫癌仍然存在于 DBT。我们研究的目的是为Mo Cancers开发成像生物标志物，以筛查女性DBT 乳房密集。这将使妇女知道自己患有MO癌的可能性，从而 让他们在补充筛查方面做出更明智的选择。关键区别 DBT和标准2D乳房X线摄影是Z方向上的可用信息。这样的其他信息 为癌症检测提供了优势，但在DBT上应用RCDT时也增加了技术复杂性 图像。有三种方法可以处理RCDT的DBT考试：1）在2D DBT切片上应用RCDT，2） 将RCDT应用于DBT的合成乳房X线照片，3）在DBT体积上应用3D RCDT。到 在筛查DBT中开发MO癌的成像生物标志物，我们需要研究最佳方法 RCDT的过程DBT。我们将使用开发方式开发三种方法的成像生物标志物 900个MO癌症病例的数据集（临床病例均为正常情况，但该妇女已在乳腺癌上检测到 她的下一个筛查DBT）和1800例（临床病例是正常的，女人没有乳房 在接下来的两个筛查DBT上检测到的癌症）。我们将利用2D卷积神经网络（CNN）和 3D CNN作为强大的分类器，用于分析RCDT处理的DBT进行MO癌症检测。使用5倍 交叉验证，我们将为每种方法训练CNN，并找到处理MO的最佳方法 癌症检测。最后，我们将使用100个情况的独立数据集来验证分类器。如果是 成功，然后每年多达1500万妇女的乳房茂密的妇女需要信息 这是他们决定获得补充筛查的决定。