Computational pathology to predict breast cancer risk in benign breast disease

 描述（由申请人提供）：当女性因通过乳房体检或筛查乳房X线检查发现的异常而接受乳房活检，并且活检的病理分析显示没有恶性肿瘤的证据时，诊断为良性乳腺疾病（BBD）。大约80%的乳腺活检显示为良性病变。BBD中的乳腺癌的识别是一个公认的，未来乳腺癌的强风险因素;然而，BBD中的乳腺癌的诊断是诊断病理学中最具挑战性的领域之一，并且已经证明很难为BBD中的非典型病变的诊断创建标准化的客观标准。小叶退化最近被证明与乳腺癌风险显著相关;然而，目前尚无临床可用的工具来量化小叶退化，因此，目前未将该特征纳入病理学报告。间质特征在乳腺癌发生的各个阶段都起着至关重要的作用;然而，定量间质特征与乳腺癌风险之间的关系从未被评估过。在这个为期两年的项目中，我们将扩展我们以前在浸润性癌症方面的工作，以开发一个计算病理学程序，用于定量评估正常乳腺和良性乳腺疾病病变中已建立的和新的形态学特征（目标1）。为了实现这一目标，我们将使用护士健康研究（NHS）事件BBD队列，其中包含来自共1758名BBD NHS参与者的组织学切片。所有病例均已由乳腺病理专家进行过回顾和注释。这些注释将广泛用于计算病理学平台的设计和评估。在我们研究的目标2中，我们将研究计算病理学（C路径）特征与未来乳腺癌风险之间的关联。为了实现这一目标，我们将使用NHS BBD乳腺癌巢式病例对照队列，该队列由613名患有BBD的女性组成，这些女性继续发展为乳腺癌，与2407名未发展的女性相匹配。使用这个独特的队列，我们将进行分析，以确定已建立的和新的C-Path衍生的形态学特征与癌症风险的关联，并确定利用C-Path预测未来癌症风险的附加值。我们项目的首要目标是开发一种新的计算系统，用于客观，定量评估BBD女性乳腺组织的既定和新的形态学特征。我们的目标是使用这个系统来获得与乳腺癌风险相关的形态学因素的生物学洞察力，并提高乳腺癌风险预测模型的性能。如果成功，我们的项目将导致开发一种临床适用的工具，该工具将提供非恶性乳腺组织中组织病理学特征的客观定量评估，以告知乳腺癌风险预测模型并指导临床决策。这一发展可能代表了正常乳腺和良性乳腺疾病病理学如何测量和用于临床实践和转化乳腺癌研究的范式转变。

 DESCRIPTION (provided by applicant): Benign breast disease (BBD) is diagnosed when a woman undergoes a breast biopsy for an abnormality found through physical breast exam or screening mammogram and pathological analysis of the biopsy shows no evidence of malignancy. Approximately 80% of breast biopsies reveal a benign lesion. The identification of atypia in BBD is a well-established, strong risk factor for future breast cancer; however, the diagnosis of atypia in BBD is one of the most challenging areas of diagnostic pathology, and it has proven difficult to create standardized objective criteria for the diagnosis of atypical lesion in BBD. Lobular involution has recently been shown to be significantly associated with breast cancer risk; however, there are currently no clinically available tools to quantitate lobular involution, and consequently, this feature is not currently incorporated into pathology reports. Stromal characteristics are known to play a crucial role in all stages of breast carcinogenesis; however, the association of quantitative stromal characteristics and breast cancer risk has never been evaluated. In this two year project, we will extend our previous work based in invasive cancer to develop a computational pathology program for the quantitative assessment of both established and novel morphological features in normal breast and benign breast disease lesions (Aim 1). To achieve this aim, we will use the Nurses' Health Study (NHS) Incident BBD cohort, which contains histological slides from a total of 1758 NHS participants with BBD. All cases have been previously reviewed and annotated by expert breast pathologists. These annotations will be used extensively in both the design and evaluation of the computational pathology platform. In Aim 2 of our study, we will examine associations between computational pathology (C- Path) features with future breast cancer risk. To achieve this aim, we will use the NHS BBD Breast Cancer Nested Case Control cohort, which consists of 613 women with BBD who went on to develop breast cancer matched to 2407 women who did not. Using this unique cohort, we will perform analyses to determine the association of established and novel C-Path derived morphological features with cancer risk and to determine the added value of utilizing C-Path to predict future cancer risk. The overriding goal of our project is to develop a new computational system for the objective, quantitative assessment of both established and novel morphologic characteristics of breast tissue in women with BBD. We aim to use this system to gain biological insight into morphologic factors associated with breast cancer risk and to improve the performance of breast cancer risk prediction models. If successful, our project will result in the development of a clinically applicable tool that will provide objective quantitative assessments of histopathological features in nonmalignant breast tissue to inform breast cancer risk prediction models and to guide clinical decisions. This development could represent a paradigm shift in how normal breast and benign breast disease pathology is measured and used in both clinical practice and translational breast cancer research.