Decision Support System for Predicting outcome of ER+ breast cancers

预测 ER 乳腺癌结果的决策支持系统

基本信息

批准号：
8123523
负责人：
Shridar Ganesan
金额：
$ 20.71万
依托单位：
IBRIS, INC.
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-09-07 至 2014-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8123523
关键词：
Adjuvant Chemotherapy Adverse effects Africa Algorithms Appearance Biological Assay Biopsy Specimen Breast Cell Nucleus China Clinical Protocols Computer Vision Systems Computer-Assisted Image Analysis Country Data Data Set Decision Support Systems Dentistry Detection Development Diagnosis Distant Estrogen receptor positive Europe Evaluation Event Exhibits Failure Gene Expression Genomics Gland Graph Guidelines Hair Health Hematoxylin and Eosin Staining Method Histologic Histopathology Hormonal Image Image Analysis Impaired cognition India Internet Label Life Lymphocyte Machine Learning Malignant Neoplasms Mammary Gland Parenchyma Marketing Measures Medical Oncologist Medicine Molecular National Comprehensive Cancer Network Nausea Neoplasm Metastasis Nuclear Outcome Ovarian Patients Pennsylvania Performance Price Recurrence Research Infrastructure Research Personnel Resolution Risk Slide Spatial Distribution Specimen Staging Staining method Stains Structure System Tamoxifen Techniques Testing Tissue Sample Toxic effect Triage Universities Woman base chemotherapy cost density digital high risk hormone therapy imaging Segmentation malignant breast neoplasm novel strategies phase 1 study prognostic tool

项目摘要

DESCRIPTION (provided by applicant): We propose an alternative, inexpensive means for identifying the subset of women with ER+ breast cancer for whom hormonal therapy alone is sufficient, and adjuvant chemotherapy is not required. Our novel approach uses computer vision and machine learning techniques to extract information from digitized histological sections of breast tissue (e.g. graphical and morphological arrangement of nuclei, lymphocytes, textural appearance, and tubule density), yielding a continuous image-based risk score (IbRiS) from zero to one that predicts the risk of recurrence. By choosing the appropriate cutoff value, IbRiS can be used to stratify ER+ patients into two classes: low and high risk. A low risk identifies those women who would respond well to hormonal therapy alone. Of the 120,000 women annually diagnosed with estrogen receptor positive (ER+) breast cancer in the US, the vast majority will be considered at high risk of having distant recurrence (metastasis) within 10 years. Under current National Comprehensive Cancer Network (NCCN) guidelines, these women will be advised to receive adjuvant chemotherapy in addition to standard hormonal treatment (e.g. tamoxifen). However, 85% of these women will not benefit from chemotherapy, and yet will still incur its deleterious side-effects. The only non-investigational tool to help better determine which women should receive tamoxifen alone is the Oncotype Dx molecular assay, which is now widely used by medical oncologists. Oncotype Dx is able to correctly reclassify (as low-risk) 50% of those women with ER+ cancers that have been classified as high-risk (or intermediate-risk) by the NCCN guidelines, obviating their need for chemotherapy. However, Oncotype Dx is inaccessible to the majority of women in the US (and worldwide) because of its high cost ($4500), and requirement that the tissue samples be sent to specialized remote facilities. Consequently, there is clearly a market need for a lower-priced assay capable of reaching a wider audience. The specific aims of this proposal are as follows: 1) develop an image-based risk score (IbRiS) for predicting the subset of women with ER+ breast cancer that wil respond wel without chemotherapy and 2) evaluate IbRiS performance in predicting recurrence over an independent set of ER+ breast cancers treated with tamoxifen alone. IbRiS has several key advantages over molecular assays. First, it requires no disruption of the current clinical protocol since the necessary tissue samples are already collected during routine pathological examinations. Second, IbRiS has a zero cost-of-goods sold, and thus could serve as either a lower-priced alternative to a molecular assay or as a quantitative triage, determining which patients should be administered the more expensive molecular test. Finally, since IbRiS only requires a digital slide scanner (i.e. no specialized facility is necessary), its footprint could extend worldwide (via the internet). PUBLIC HEALTH RELEVANCE: Every year tens of thousands of women in the US with estrogen receptor positive (ER+) breast cancer are treated with chemotherapy, though only a few thousand will benefit from it. In this proposal we will develop an image-based risk score (IbRiS) to predict which women with ER+ breast cancer do not require chemotherapy. This test will provide an economical alternative to the far more expensive gene-expression based assays currently in use.

描述（由申请人提供）：我们提出了一种替代，廉价的手段，用于识别单独使用激素治疗的ER+乳腺癌女性的子集，并且不需要辅助化疗。我们的新方法使用计算机视觉和机器学习技术来从乳房组织的数字化组织学切片中提取信息（例如核，淋巴细胞的图形和形态排列，淋巴细胞，质地外观和小管密度），从而预测从一个零到一个基于图像的风险评分（IBRIS），从而预测复发风险的风险。通过选择适当的临界值，IBRI可用于将ER+患者分为两类：低风险和高风险。低风险可以确定那些仅对荷尔蒙治疗反应良好的妇女。在美国，每年被诊断为雌激素受体阳性（ER+）乳腺癌的120,000名妇女中，绝大多数将在10年内被视为具有远处复发（转移）的高风险。根据当前国家综合癌症网络（NCCN）指南，将建议这些妇女除标准激素治疗（例如他莫昔芬）外接受辅助化疗。但是，这些女性中有85％不会从化学疗法中受益，但仍会引起其有害的副作用。唯一有助于更好地确定哪些女性应接受他莫昔芬的非投票工具是DX分子测定法，该测定现已被医学肿瘤学家广泛使用。 Oncotype DX能够正确地重新分类（低风险）50％的患有ER+癌症的女性，这些女性已通过NCCN指南归类为高危（或中等风险），从而消除了她们对化学疗法的需求。但是，由于其成本很高（4500美元），因此DX的ONCOTYPE DX无法访问美国（和全球）的大多数女性，并且要求将组织样品发送到专用远程设施。因此，显然市场需要一个能够吸引更多受众的低价测定法。该提案的具体目的如下：1）开发一种基于图像的风险评分（IBRI），以预测ER+乳腺癌女性的子集，而乳腺癌的妇女将不进行化学疗法而反应WEL和2）评估IBRIS在预测单独使用他莫昔芬的独立的ER+乳腺癌的复发方面评估IBR的表现。 ibris比分子测定具有多个关键优势。首先，它不需要破坏当前的临床方案，因为在常规病理检查中已经收集了必要的组织样品。其次，ibris的货量为零，因此可以用作分子测定的价格较低的替代品或定量分类，确定应该对哪些患者进行更昂贵的分子测试。最后，由于Ibris仅需要数字幻灯片扫描仪（即无需专门的设施），因此其足迹可以在全球范围内扩展（通过Internet）。公共卫生相关性：每年美国成千上万的女性患有雌激素受体阳性（ER+）乳腺癌的妇女都会接受化学疗法治疗，尽管只有几千人将受益于此。在此提案中，我们将开发一个基于图像的风险评分（IBRI），以预测哪些ER+乳腺癌的女性不需要化学疗法。该测试将为目前正在使用的基于基因表达的基因表达的测定法提供一种经济的替代方法。