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

基本信息

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
项目类别：
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

项目摘要

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.

大多数在筛查乳房 X 光检查时乳房致密的美国女性都会收到一封通知信乳房X光检查对她们来说效果较差，而致密的乳房会增加患乳腺癌的风险。这封信建议女性与她们的医生讨论是否应该进行额外的筛查超声波或磁共振成像 (MRI)。额外筛查的可能好处是检测乳腺X线隐匿性（MO）癌症。然而，女性漏诊癌症的可能性并不已知。因此，女性面临着一个艰难的决定，平衡额外的不确定的潜在好处针对已知成本进行筛选。这些已知成本是财务成本（因为有些州不承担这些费用）补充筛查）以及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光检查。我们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性来突出微妙的可疑信号左右乳房X光检查之间。我们的技术使用 ROC 曲线下面积达到 0.81 筛查乳房X光检查。数字乳腺断层合成 (DBT) 是一种伪 3D 成像技术，正在取代美国的乳房X光检查，因为其敏感性和特异性更高。然而，MO 癌症仍然存在于二丁基锡。我们研究的目标是开发 MO 癌症的成像生物标志物，用于筛查女性 DBT 与致密的乳房。这将使女性知道她们患有 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。之间的主要区别 DBT 和标准 2D 乳房 X 线摄影是 z 方向上的可用信息。此类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。可以通过三种方式处理 RCDT 的 DBT 检查：1) 在 2D DBT 切片上应用 RCDT，2) 将 RCDT 应用于 DBT 合成乳房 X 光照片，以及 3) 将 3D RCDT 应用于 DBT 体积。到开发MO癌症的影像生物标志物来筛查DBT，我们需要研究最佳方法为 RCDT 处理 DBT。我们将使用发育模型为这三种方法开发成像生物标志物 900 个 MO 癌症病例的数据集（临床病例读数正常，但该妇女在她的下一次筛查 DBT）和 1800 例（临床病例解读为正常且该妇女没有乳房）在接下来的两次 DBT 筛查中发现了癌症）。我们将利用 2D 卷积神经网络 (CNN) 和 3D CNN 作为强大的分类器来分析 RCDT 处理的 DBT，以进行 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 DBT 的最佳方法癌症检测。最后，我们将使用包含 100 个案例的独立数据集来验证分类器。如果我们是如果成功的话，每年将有多达 1500 万拥有致密乳房的女性需要相关信息这是他们决定接受补充筛查的依据。