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

在美国，大多数乳房致密的女性在筛查乳房X光检查时都会收到一封通知她们的信 乳房X光检查对他们来说效果较差，乳房致密会增加患乳腺癌的风险。 这封信建议妇女与她们的医生讨论她们是否应该进行额外的筛查 超声或磁共振成像(MRI)。额外筛查的可能好处是检测到 乳房X光摄影隐匿性(MO)癌。然而，女性漏掉癌症的可能性并不是 为人所知。因此，女性面临着一个艰难的选择，平衡额外的潜在利益。 对照已知的成本进行筛选。这些已知成本是财务成本(因为一些州不包括 补充筛查)和不必要的活检风险，因为超声和MRI的特异性较低 而不是乳房X光检查。我们已经开发了一种使用氡累积分布变换的新技术 (RCDT)检测MO癌症。RCDT可以通过检测不对称来突出细微的可疑信号 在左乳房X光和右乳房X光之间。我们的技术在ROC曲线下实现了0.81的面积 筛查乳房X光检查。数字乳房断层合成(DBT)，一种伪3D成像技术，正在取代 美国的乳房X光检查，因为它具有更高的敏感性和特异度。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癌的成像生物标记物 丰满的乳房。这将使女性知道她们患上MO癌症的可能性，因此， 允许他们在补充筛查方面做出更明智的选择。两者之间的主要区别是 在z方向上，DBT和标准的2D乳房X光摄影是可用的信息。这样的补充信息 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。有三种方法处理RCDT的DBT检查：1)在2D DBT切片上应用RCDT，2) 将RCDT应用于来自DBT的合成乳房X光照片，以及3)将3D RCDT应用于DBT体积。至 开发MO癌的影像生物标记物在筛查DBT时，需要研究最佳的方法 为RCDT处理DBT。我们将为这三种方法开发成像生物标记物，使用开发的 900例MO癌症病例的数据集(临床病例读起来正常，但妇女在 她的下一次筛查)和1800例(临床病例读作正常，该妇女没有乳房 在接下来的两次DBT筛查中发现癌症)。我们将利用2D卷积神经网络(CNN)和 3D CNN作为稳健的分类器来分析RCDT处理的DBT用于MO癌症检测。使用5倍的 交叉验证，我们将为每种方法训练CNN，并找到处理DBT for MO的最佳方法 癌症检测。最后，我们将使用一个包含100个案例的独立数据集来验证该分类器。如果我们是 如果成功，那么每年将有多达1500万乳房致密的女性需要了解 以此作为他们进行补充筛查的依据。

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.