Computational pathology to predict breast cancer risk in benign breast disease

计算病理学预测良性乳腺疾病的乳腺癌风险

• 批准号: 8877779
• 负责人: Andrew H Beck
• 金额: $ 24.78万
• 依托单位: BETH ISRAEL DEACONESS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-06 至 2017-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8877779
• 关键词: Area; Atypia; Benign; Biological; Biopsy; Breast; Breast Cancer Epidemiology; Breast Cancer Prevention; Breast Carcinogenesis; Breast Diseases; Breast biopsy; Cancer Diagnostics; Characteristics; Classification; Clinical; Computer Analysis; Computer Simulation; Data; Descriptor; Development; Diagnosis; Diagnostic; Epithelial; Evaluation; Fatty acid glycerol esters; Future; Goals; Heterogeneity; Image; Image Analysis; Laboratories; Lesion; Lobular; Lobule; Malignant Neoplasms; Mammary Gland Parenchyma; Mammography; Measurement; Measures; Methods; Modeling; Morphology; Non-Malignant; Normal tissue morphology; Nurses' Health Study; Palpable; Participant; Pathologist; Pathology; Pathology Report; Performance; Phenotype; Play; Prevention; Research Personnel; Risk; Risk Assessment; Risk Factors; Role; Slide; Staging; System; Systems Analysis; Tissues; Translational Research; Woman; Women' s Group; Work; anticancer research; base; cancer risk; case control; clinical care; clinical practice; clinically significant; cohort; design; disease diagnosis; epidemiology study; high risk; improved; insight; interest; malignant breast neoplasm; novel; predictive modeling; prognostic; programs; public health relevance; screening; tool

 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.

 描述（由申请人提供）：良性乳腺疾病 (BBD) 是指女性因乳房检查或筛查性乳房 X 光检查发现异常而进行乳房活检，且活检病理分析未显示恶性肿瘤证据时，即可诊断为良性乳腺疾病 (BBD)。大约 80% 的乳房活检显示良性病变。 BBD 中异型性的鉴定是未来乳腺癌的一个明确的、强烈的危险因素；然而，BBD 的非典型性诊断是诊断病理学中最具挑战性的领域之一，并且事实证明很难为 BBD 的非典型病变的诊断建立标准化的客观标准。最近发现小叶复旧与乳腺癌风险显着相关。然而，目前临床上没有可用的工具来定量小叶复旧，因此，该功能目前尚未纳入病理报告中。众所周知，基质特征在乳腺癌发生的各个阶段都发挥着至关重要的作用。然而，定量基质特征与乳腺癌风险之间的关联从未得到评估。在这个为期两年的项目中，我们将扩展之前基于浸润性癌症的工作，开发计算病理学程序，用于定量评估正常乳腺和良性乳腺疾病病变中已确定的和新的形态特征（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，其中包含总共 1758 名患有 BBD 的 NHS 参与者的组织学切片。所有病例均已由乳腺病理学家专家进行过审查和注释。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，我们将检查计算病理学 (C-Path) 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，这些女性后来患上了乳腺癌，而 2407 名没有患乳腺癌的女性则与之相匹配。利用这个独特的队列，我们​​将进行分析，以确定已建立的和新的 C-Path 衍生形态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价值。我们项目的首要目标是开发一种新的计算系统，以客观、定量地评估 BBD 女性乳腺组织的既定形态特征和新形态特征。我们的目标是利用该系统从生物学角度深入了解与乳腺癌风险相关的形态因素，并提高乳腺癌风险预测模型的性能。如果成功，我们的项目将开发出一种临床适用的工具，对非恶性乳腺组织的组织病理学特征进行客观的定量评估，为乳腺癌风险预测模型提供信息并指导临床决策。这一发展可能代表了正常乳腺和良性乳腺疾病病理学在临床实践和转化乳腺癌研究中测量和使用方式的范式转变。