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.

在美国，大多数在筛查乳房X光检查时有致密乳房的妇女都会收到一封通知信， 乳房X光检查对她们不太有效，乳房致密会增加患乳腺癌的风险。 这封信建议女性与医生讨论是否应该进行额外的筛查， 超声或磁共振成像（MRI）。额外筛查的可能益处是检测出 乳房X线摄影隐匿性（MO）癌症。然而，女性错过癌症的可能性并不是 知道的因此，妇女面临着一个困难的决定，要在额外的收入和不确定的潜在利益之间做出平衡。 根据已知成本进行筛选。这些已知的费用是财政费用（因为有些州不支付 补充筛选）和不必要的活检的风险，因为超声和MRI的特异性较低 乳房X光检查我们开发了一种新的技术，使用氡累积分布变换 （RCDT）来检测MO癌症。RCDT可以通过检测不对称性来突出微妙的可疑信号 左右乳房X光片的区别我们的技术实现了ROC曲线下面积为0.81， 筛查乳房X光片数字乳腺断层合成摄影（DBT），一种伪3D成像技术，正在取代 乳腺X线摄影在美国，因为其较高的灵敏度和特异性。然而，MO癌症仍然存在于 DBT。我们的研究目标是开发用于筛查女性DBT的MO癌症的成像生物标志物 有着高密度的乳房这将使妇女知道她们患MO癌症的可能性，从而， 让他们在补充筛查方面做出更明智的选择。的关键区别 DBT和标准2D乳腺X射线摄影是z方向上的可用信息。这种附加信息 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检查：1）在2D DBT切片上应用RCDT，2） 对来自DBT的合成乳房X线照片应用RCDT，以及3）对DBT体积应用3D RCDT。到 在筛查DBT中开发MO癌症的成像生物标志物，我们需要研究最佳方法， 为RCDT处理DBT。我们将开发成像生物标志物的三种方法，使用发展 900个MO癌症病例的数据集（临床病例读作正常，但该妇女在2010年1月1日检测到乳腺癌） 她的下一次筛查DBT）和1800例（临床病例读作正常，女性没有乳房 在她接下来的两次筛查DBT中检测到的癌症）。我们将利用2D卷积神经网络（CNN）， 3D CNN作为鲁棒分类器来分析用于MO癌症检测的RCDT处理的DBT。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 DBT的最佳方法 癌症检测最后，我们将使用100个案例的独立数据集来验证分类器。如果我们 如果成功，那么每年有多达1500万患有致密乳房的妇女将需要有关 这是他们决定进行补充筛查的依据