Computerized histologic image predictor of cancer outcome

癌症结果的计算机组织学图像预测器

基本信息

  • 批准号:
    9305968
  • 负责人:
  • 金额:
    $ 62.5万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2016
  • 资助国家:
    美国
  • 起止时间:
    2016-07-01 至 2021-06-30
  • 项目状态:
    已结题

项目摘要

SUMMARY: There is an increased need for predictive and prognostic assays to distinguish more and less aggressive phenotypes of cancer due to A) dramatic increase in cancer incidence and; B) improvements in early diagnosis. Predictive assays in particular will allow for patients with less aggressive disease to be spared more aggressive treatment. Most prognostic tests in the US and Europe are based on gene expression assays (e.g. Oncotype DX (ODx)). Recent studies have shown extensive genetic heterogeneity among cancer cells between tumors and even within the same tumor, suggesting that approaches for recommending therapy for a patient based on the “average” molecular signal of many cells are overly simplistic. Interestingly, for a number of cancers, tumor grade (morphologic appearance on tissue as assessed qualitatively or semi-quantitatively by a pathologist) has been found to be highly correlated with disease outcome. However pathologic grade tends to suffer from significant inter-observer variability. Digitzation of histological samples, or whole slide imaging, facilitates a quantitative approach towards evaluating disease progression and predicting outcome, while also facilitating the adoption of telepathology. Recently, research groups (including our own) have begun to show that computer extracted measurements of tumor morphology (e.g. capturing nuclear orientation, texture, shape, architecture) from routine H&E stained cancer tissue images can predict disease aggressiveness and treatment outcome. By computationally interrogating the entire tumor landscape and its most invasive elements from a standard H&E slide, these approaches can allow for more accurate capture of tumor heterogeneity, disease risk and hence the most appropriate treatment strategy. The goal of this academic-industrial partnership is to develop and validate a computerized histologic image-based predictor (CHIP) to identify which early-stage, estrogen receptor positive (ER+) breast cancer patients are candidates for hormonal therapy alone and which women are candidates for adjuvant chemotherapy based off analysis of the pathology slides derived from biopsy and surgical specimens. Inspirata Inc., a cancer diagnostics company which has recently licensed a number of histomorphometry based technologies from the Madabhushi group, will bring quality management systems and production software standards to help create a pre-commercial companion diagnostic test of the CHIP assay. Additionally Inspirata Inc. will build a complete regulatory pathway for successful translation of the assay in the US and abroad. Finally, the pre-commercial prototype of the CHIP assay will be independently validated using the same strategy and data cohorts as ODx. Our approach has several advantages over molecular assays such as ODx in that it (1) can interrogate the entire expanse of the pathology image enabling a more accurate capture of tumor heterogeneity and hence disease risk, (2) is non-disruptive of pathology workflow, (3) non-destructive of tissue and would be substantially (4) cheaper (critical in low to middle income countries) and (5) faster.
总结:越来越需要预测和预后分析来区分更多和更少的 由于A)癌症发病率的急剧增加和; B) 早期诊断特别是预测性检测将使侵袭性较低的疾病患者得以幸免 更积极的治疗。美国和欧洲的大多数预后测试都是基于基因表达测定 (e.g. Oncotype DX(ODx))。最近的研究表明癌细胞之间存在广泛的遗传异质性 肿瘤之间,甚至在同一肿瘤内,这表明,推荐治疗方法, 基于许多细胞的“平均”分子信号来诊断患者是过于简单化的。 有趣的是,对于许多癌症,肿瘤分级(如评估的组织上的形态学外观)与肿瘤分级相关。 由病理学家定性或半定量)与疾病高度相关 结果。然而,病理分级往往遭受显着的观察者间的变异性。数字化 组织学样本或全载玻片成像有助于评估疾病的定量方法 进展和预测结果,同时也促进远程病理学的采用。最近,研究 研究小组(包括我们自己的)已经开始表明,计算机提取的肿瘤形态学测量结果 (e.g.捕获核取向、纹理、形状、结构)从常规H&E染色的癌组织图像 可以预测疾病的侵袭性和治疗结果。通过计算分析整个肿瘤 景观及其最具侵入性的元素,这些方法可以允许更多 准确捕获肿瘤异质性、疾病风险,从而获得最合适的治疗策略。 该学术-工业合作伙伴关系的目标是开发和验证计算机组织学 基于图像的预测(CHIP),以确定哪些早期,雌激素受体阳性(ER+)乳腺癌 患者是单独激素治疗的候选者,哪些女性是辅助治疗的候选者 化疗基于对活检和手术标本的病理切片的分析。Inspirata 股份有限公司、一家癌症诊断公司最近获得了一些基于组织形态学的许可, 来自Madabhushi集团的技术,将带来质量管理系统和生产软件, 标准,以帮助创建CHIP测定的商业化前伴随诊断测试。此外,Inspira Inc.将建立一个完整的监管途径,在美国和国外成功翻译的测定。 最后,CHIP测定的预商业化原型将使用相同的方法独立验证。 策略和数据队列作为ODx。我们的方法与ODx等分子测定相比具有几个优势 因为它(1)可以询问病理图像的整个范围, 肿瘤异质性和因此的疾病风险,(2)不破坏病理学工作流程,(3)不破坏 并且将大大(4)更便宜(在低收入到中等收入国家中至关重要)和(5)更快。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(36)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

MICHAEL D FELDMAN其他文献

MICHAEL D FELDMAN的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('MICHAEL D FELDMAN', 18)}}的其他基金

Software to facilitate multimode, multiscale fused data for Pathology and Radiolo
用于促进病理学和放射学多模式、多尺度融合数据的软件
  • 批准号:
    8305155
  • 财政年份:
    2009
  • 资助金额:
    $ 62.5万
  • 项目类别:
Software to facilitate multimode, multiscale fused data for Pathology and Radiolo
用于促进病理学和放射学多模式、多尺度融合数据的软件
  • 批准号:
    8512667
  • 财政年份:
    2009
  • 资助金额:
    $ 62.5万
  • 项目类别:
Software to facilitate multimode, multiscale fused data for Pathology and Radiolo
用于促进病理学和放射学多模式、多尺度融合数据的软件
  • 批准号:
    7566209
  • 财政年份:
    2009
  • 资助金额:
    $ 62.5万
  • 项目类别:
Software to facilitate multimode, multiscale fused data for Pathology and Radiolo
用于促进病理学和放射学多模式、多尺度融合数据的软件
  • 批准号:
    8192918
  • 财政年份:
    2009
  • 资助金额:
    $ 62.5万
  • 项目类别:
ACC BioRepository
ACC生物样本库
  • 批准号:
    10550250
  • 财政年份:
    1997
  • 资助金额:
    $ 62.5万
  • 项目类别:
ACC BioRepository
ACC生物样本库
  • 批准号:
    10088758
  • 财政年份:
    1997
  • 资助金额:
    $ 62.5万
  • 项目类别:
ACC BioRepository
ACC生物样本库
  • 批准号:
    10330978
  • 财政年份:
    1997
  • 资助金额:
    $ 62.5万
  • 项目类别:

相似海外基金

3D Engineered Model of Microscopic Colorectal Cancer Liver Metastasis for Adjuvant Chemotherapy Screens
用于辅助化疗筛选的显微结直肠癌肝转移 3D 工程模型
  • 批准号:
    10556192
  • 财政年份:
    2023
  • 资助金额:
    $ 62.5万
  • 项目类别:
Developing Digital Pathology Biomarkers for Response to Neoadjuvant and Adjuvant Chemotherapy in Breast Cancer
开发数字病理学生物标志物以应对乳腺癌新辅助和辅助化疗
  • 批准号:
    10315227
  • 财政年份:
    2021
  • 资助金额:
    $ 62.5万
  • 项目类别:
Circulating Tumour DNA Analysis Informing Adjuvant Chemotherapy in Stage III Colorectal Cancer: A Multicentre Phase II/III Randomised Controlled Trial (DYNAMIC-III)
循环肿瘤 DNA 分析为 III 期结直肠癌辅助化疗提供信息:多中心 II/III 期随机对照试验 (DYNAMIC-III)
  • 批准号:
    443988
  • 财政年份:
    2021
  • 资助金额:
    $ 62.5万
  • 项目类别:
    Operating Grants
Establishment of new selection system for adjuvant chemotherapy of colorectal cancer
结直肠癌辅助化疗新选择体系的建立
  • 批准号:
    20K09011
  • 财政年份:
    2020
  • 资助金额:
    $ 62.5万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
Improved survival by Helicobacter pylori-modulated immunity in gastric cancer patients with adjuvant chemotherapy
幽门螺杆菌调节免疫力可改善接受辅助化疗的胃癌患者的生存率
  • 批准号:
    19K09130
  • 财政年份:
    2019
  • 资助金额:
    $ 62.5万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
A new strategy of adjuvant chemotherapy for lung cancer based on the expression of anti-aging gene Klotho
基于抗衰老基因Klotho表达的肺癌辅助化疗新策略
  • 批准号:
    19K18225
  • 财政年份:
    2019
  • 资助金额:
    $ 62.5万
  • 项目类别:
    Grant-in-Aid for Early-Career Scientists
Novel candidate factors predicting the effect of S-1 adjuvant chemotherapy of pancreatic cancer
预测胰腺癌S-1辅助化疗效果的新候选因素
  • 批准号:
    18K16337
  • 财政年份:
    2018
  • 资助金额:
    $ 62.5万
  • 项目类别:
    Grant-in-Aid for Early-Career Scientists
Project 2-Metabolic Modulation of Myeloid-Derived Suppressor Cells to Increase Efficacy of Neo adjuvant Chemotherapy and Immunotherapy
项目2-骨髓源性抑制细胞的代谢调节以提高新辅助化疗和免疫疗法的疗效
  • 批准号:
    10005254
  • 财政年份:
    2018
  • 资助金额:
    $ 62.5万
  • 项目类别:
Radiogenomic tools for prediction of breast cancer neo-adjuvant chemotherapy response from pre-treatment MRI
通过治疗前 MRI 预测乳腺癌新辅助化疗反应的放射基因组学工具
  • 批准号:
    9763320
  • 财政年份:
    2018
  • 资助金额:
    $ 62.5万
  • 项目类别:
Analysis of the molecular mechanism for the prognostic biomarker of adjuvant chemotherapy
辅助化疗预后生物标志物的分子机制分析
  • 批准号:
    18K07341
  • 财政年份:
    2018
  • 资助金额:
    $ 62.5万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了