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中文摘要
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描述(由申请人提供):鉴于贝伐单抗最近获得批准,但这些疗法的反应率适中,副作用具有挑战性,因此对最严重形式的脑癌胶质母细胞瘤的抗血管生成疗法进行评估是非常及时的。迫切需要临床决策工具;幸运的是,我们最近发表的数据表明,使用MRI和基于钆的方法测量肿瘤的微血管特性可能非常有用,因为通过适当的定量,这些方法似乎能够作为有效的预后成像生物标志物,并可能与血液生物标志物有益地结合。我们建议加入NCI的定量成像网络(QIN),并开发改进的动态对比增强MRI和动态敏感性MRI分析方法,以提高量化和减少可变性。我们建议通过对接受抗血管生成治疗的患者进行自下而上的模拟、模拟研究、回顾性分析和前瞻性分析,开发适用于多中心环境的技术。我们预计,我们提出的方法,特别是通过与秦密切协调,将提高先进微血管MRI方法作为潜在成像生物标志物的可靠性,并为临床有用的决策工具铺平道路。相关性(见说明):先进的MRI方法可以提高我们提供准确预后的能力,并可能指导胶质母细胞瘤患者的治疗选择。我们提出的研究将有助于建立一种通用的、标准化的方法来获取和分析两种形式的血管MRI,这两种形式已经显示出极好的前景。我们将通过仔细减少可变性和密切参与国家癌症研究所的定量成像网络来做到这一点。这些努力将使这些先进技术得到更广泛的应用,并更适当地确定它们对患者的益处。
英文摘要
DESCRIPTION (provided by applicant): Assessment of anti-angiogenic therapies for the most severe form of brain cancer, glioblastoma, is extremely timely given the recent approval of bevacizumab yet the moderate response rate and the challenging side effects of these therapies. Clinical decision-making tools are badly needed; fortunately, our recently published data suggest that measurement of microvascular properties of the tumor using MRI and gadolinium-based approaches could be very useful, as with proper quantitation these methods appear to be capable of serving as an effective prognostic imaging biomarker, and may be beneficially combined with blood biomarkers. We propose to join the NCI's Quantitative Imaging Network (QIN) and develop improved analysis methods for dynamic contrast enhanced MRI and dynamic susceptibility MRI that will improve quantification and decrease variability. We propose to develop techniques that will be applicable in the multicenter setting through a bottom-up approach of simulations, phantom studies, retrospective analysis, and prospective analysis in patients undergoing treatment with anti-angiogenic therapies. We anticipate that our proposed approach, in particular through working in close harmony with the QIN, will improve the reliability of advanced microvascular MRI methods as potential imaging biomarkers, and pave the way for a clinically useful decision-making tool. RELEVANCE (See instructions): Advanced MRI methods may improve our ability to provide an accurate prognosis and potentially guide treatment choices for glioblastoma patients. Our proposed research will help establish a common, standardized approach to acquisition and analysis of two forms of vascular MRI that have shown excellent promise. We will do this by careful reduction of variability and by close participation in the National Cancer Institute's Quantitative Imaging Network. These efforts will enable these advanced techniques to become more widely available and more appropriately establish their benefit to patients.
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Robust AI to develop risk models in retinopathy of prematurity using deep learning
  • 批准号:
    10254429
  • 项目类别:
  • 资助金额:
    $19.69万
  • 财政年份:
    2020
  • 负责人:
    Jayashree Kalpathy-Cramer
  • 依托单位:
Distributed Learning of Deep Learning Models for Cancer Research
  • 批准号:
    10228687
  • 项目类别:
  • 资助金额:
    $39.48万
  • 财政年份:
    2019
  • 负责人:
    Jayashree Kalpathy-Cramer
  • 依托单位:
Distributed Learning of Deep Learning Models for Cancer Research
  • 批准号:
    10018827
  • 项目类别:
  • 资助金额:
    $39.48万
  • 财政年份:
    2019
  • 负责人:
    Jayashree Kalpathy-Cramer
  • 依托单位:
Informatics Tools for Optimized Imaging Biomarkers for Cancer Research&Discovery
  • 批准号:
    9564836
  • 项目类别:
  • 资助金额:
    $67.56万
  • 财政年份:
    2014
  • 负责人:
    Jayashree Kalpathy-Cramer
  • 依托单位:
海外基金