Breast Cancer Detection Consortium

摘要 乳房X光摄影是乳腺癌的一种早期检测手段， 在美国，已经建立了绩效基准，并在大多数研究中 全世界都已证明可以降低因该病造成的死亡率。这 相对便宜的乳房x光成像也提供了一种可以直接 通过针刺活组织检查进行采样，通常导致明确的病理诊断 浸润性癌、原位癌或良性病变。没有一个系统是完美的 乳房X光检查，特别是在美国，每年有超过160万例活检 检测大约230,000例浸润性癌和60,000例非浸润性癌的阳性 预测价值不到20%。这一点可能有很大的改进空间， 减少活组织检查的数量，但这一改进不能牺牲检测率，因此 阴性预测值(NPV，识别真实阴性)必须保持非常高的水平。在这 生物标记物开发实验室的应用，我们建议测试是否结合 我们已经确定的乳房X光检查特征分析和候选生物标志物可以实现 患者和提供者可以接受的NPV，以防止不必要的乳房 活组织检查。其中一个生物标记物是一种循环中的巨细胞，被称为“癌症相关” 巨噬细胞样“(CAML)只能用新鲜提取的全血检测到，我们 建议在杜克大学对患有乳腺癌的女性进行前瞻性试验 诊断。我们的现实目标是在四年的时间里培养大约1000名女性 已经进行了现场数字乳房X光检查。图像将进行特征提取，以 决策建模。血液将被分析CAML细胞的存在和类型， 使用Stephen Johnston开发的高密度多肽阵列进行免疫签名 以及对两种特定分析物的测量，这些分析物具有最高的灵敏度和 对基底细胞癌、CA125和TP53自身抗体的特异性。作为要素分析来自 研究称，至少对于乳房X光检查的肿块来说，仅靠成像就可以达到~0.9的AUC。 旨在确定生物标记物是否具有足够的补充信息 成像和彼此将AUC增加到0.95，从而使我们能够确定阈值 净现值为98%。我们将采用最谨慎、最一致的标准操作 程序，最佳候选生物标志物，以及最完善的成像算法 让这项研究成为一项明确的研究。

Abstract Mammography is an early detection modality for breast cancer that is implemented widely in the United States, has established benchmarks of performance, and in most studies throughout the world has been demonstrated to reduce mortality due to the disease. This relatively inexpensive x-ray imaging of the breast also provides a location that can be directly sampled through needle biopsy which leads generally to an unambiguous pathologic diagnosis of invasive cancer, carcinoma in situ, or benign findings. No system is perfect and mammographic screening, particularly in the US, prompts over 1.6 million biopsies per year detecting approximately 230,000 invasive and 60,000 non-invasive cancers for a positive predictive value of less than 20%. There may be substantial room to improve on this and reduce the number of biopsies but this improvement must not sacrifice detection rates so the negative predictive value (NPV, identification of true negatives) must remain very high. In this Biomarker Development Laboratory application, we propose to test whether a combination of mammographic feature analysis and candidate biomarkers that we have identified can achieve an NPV that would be acceptable to patients and providers to prevent unnecessary breast biopsies. One of the biomarkers is a type of circulating giant cell termed “Cancer Associated Macrophage Like” (CAML) that can only be detected using freshly drawn whole blood, we propose to conduct a prospective trial at Duke University in women undergoing breast cancer diagnosis. Our realistic goal is to accrue ~1000 women over the course of 4 years for which full field digital mammography has been performed. The images will undergo feature extraction for decision modeling. Blood will be analyzed for the presence and type of CAML cells, immunosignaturing using the high density peptide arrays developed by Stephen Johnston at Arizona State, and measurements of two specific analytes that have the highest sensitivity and specificity for basal type cancers, CA125 and TP53 autoantibodies. As feature analysis from imaging alone can achieve, at least for masses on mammography, an AUC of ~0.9, the study is designed to determine whether the biomarkers have sufficient complementary information to the imaging and each other to increase the AUC to 0.95 allowing us to identify a threshold where there is a 98% NPV. We will make use of the most careful and consistent standard operating procedures, the best candidate biomarkers, and the most well developed imaging algorithms to make this a definitive study.