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（由适用提供）：当妇女经过乳房活检以通过身体乳房检查或筛查乳房X线照片和活检的病理分析发现的绝对乳房活检时，诊断出良性乳房疾病（BBD）。大约80％的乳房活检揭示了良性病变。 BBD中非典型性的鉴定是未来乳腺癌的良好，强大的危险因素。然而，BBD中非典型的诊断是诊断病理学中最挑战的领域之一，事实证明，为BBD中非典型病变的诊断病理学创建标准化的客观标准很难。最近已证明叶的相互作用与乳腺癌风险显着相关。但是，目前尚无临床上可用的工具来量化小叶的相关性，因此，此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所有阶段都起着至关重要的作用。但是，从未评估过定量基质特征与乳腺癌风险的关联。在这个为期两年的项目中，我们将扩展我们以前的基于侵入性癌症的工作，以开发一项计算病理学计划，以定量评估正常乳腺癌和良性乳腺癌病变中已建立的形态学和新型形态学特征（AIM 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BBD共有1758 NHS参与者的组织学幻灯片。所有病例先前均已由专家乳房病理学家进行了审查和注释。这些注释将在计算病理平台的设计和评估中广泛使用。在我们研究的目标2中，我们将研究计算病理学（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乳腺癌嵌套的病例对照组，该病例对照组由613名BBD女性组成，这些女性继续发展为乳腺癌，与2407名女性相匹配。使用这种独特的队列，我们​​将进行分析，以确定已建立和新颖的C-Path得出的形态学特征与癌症风险的关联，并确定使用C-Path预测未来癌症风险的附加值。我们项目的压倒性目标是开发一种新的计算系统，以对BBD女性的乳腺组织的乳腺组织建立和新颖的形态学特征进行客观，定量评估。我们的目的是利用该系统来获得对与乳腺癌风险相关的形态学因素的生物学见解，并改善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临床上适用的工具，该工具将对非恶性乳腺组织中的组织病理学特征进行客观的定量评估，以告知乳腺癌风险预测模型并指导临床决策。这种发展可能代表了在临床实践和翻译乳腺癌研究中测量和使用正常乳房和良性乳腺病病理学的范式转变。