权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

基本信息

批准号：
9047258
负责人：
Rulla M Tamimi
金额：
$ 19.14万
依托单位：
BETH ISRAEL DEACONESS MEDICAL CENTER
依托单位国家：
美国
项目类别：
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/9047258
关键词：
Area Assessment tool 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 Health 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&apos Health Study Palpable Participant Pathologist Pathology Pathology Report Performance Phenotype Play Prevention Research Personnel Risk Risk Assessment Risk Factors Role Staging System Systems Analysis Tissues Translational Research Woman Women&apos s Group Work anticancer research base cancer risk case control clinical care clinical practice clinically significant cohort design disease diagnosis epidemiology study high risk histological slides improved insight interest malignant breast neoplasm novel predictive modeling prognostic programs screening tool

项目摘要

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(申请人提供)：良性乳腺疾病(BBD)是指女性接受乳房活检，发现乳房体格检查或筛查乳房X光检查发现异常，但病理分析显示没有恶性病变的证据。大约80%的乳腺活检显示为良性病变。BBD中异型性的识别是一个公认的、未来乳腺癌的强烈危险因素；然而，BBD中异型性的诊断是诊断病理学中最具挑战性的领域之一，并且已经被证明很难为BBD中不典型病变的诊断建立标准化的客观标准。小叶退缩最近被证明与乳腺癌的风险显著相关；然而，目前临床上还没有可用的工具来量化小叶退缩，因此，这一特征目前没有被纳入病理报告。已知间质特征在乳腺癌发生的所有阶段都起着关键作用；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，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的既有和新的形态特征(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。所有病例以前都经过专家乳腺病理学家的审查和注释。这些注释将在计算病理学平台的设计和评估中广泛使用。在我们研究的目标2中，我们将检查计算病理学(C-Path)特征与未来乳腺癌风险之间的关系。为了实现这一目标，我们将使用NHS BBD乳腺癌嵌套病例对照队列，该队列包括613名患有BBD的女性和2407名未患乳腺癌的女性。利用这一独特的队列，我们将进行分析，以确定已建立的和新的C路径派生的形态特征与癌症风险的关联，并确定利用C路径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、定量地评估患有BBD的女性乳房组织的既有和新的形态特征。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因素的生物学洞察力，并改进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，该工具将为非恶性乳腺组织的组织病理学特征提供客观的定量评估，为乳腺癌风险预测模型提供信息，并指导临床决策。这一进展可能代表着在临床实践和转化性乳腺癌研究中衡量和使用正常乳腺和良性乳腺疾病病理的方式发生了范式转变。