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    Automated Reduction of Biomarkers
    1.
    发明申请
    Automated Reduction of Biomarkers 审中-公开
    自动降低生物标志物

    公开(公告)号：US20090006055A1

    公开(公告)日：2009-01-01

    申请号：US12135313

    申请日：2008-06-09

    申请人： Glenn Fung ,  Renaud G. Seigneuric ,  Sriram Krishnan ,  R. Bharat Rao ,  Philippe Lambin

    发明人： Glenn Fung ,  Renaud G. Seigneuric ,  Sriram Krishnan ,  R. Bharat Rao ,  Philippe Lambin

    IPC分类号： G06G7/60

    CPC分类号： G16B25/00 ,  G16B40/00

    摘要： A list of biomarkers indicative of patient outcome is reduced. A computer program is applied to a set of biomarkers indicative of a patient outcome (e.g., prognosis, diagnosis, or treatment result). The computer program models the set of biomarkers with a subset of the biomarkers. The subset is identified without labeling based on the patient outcome. Instead, biomarker scores (e.g., sequence score) are used to identify the subset of biomarkers.

    摘要翻译： 减少了指示患者结果的生物标志物的列表。 将计算机程序应用于指示患者结果的一组生物标志物（例如，预后，诊断或治疗结果）。 计算机程序用生物标志物的一个子集建模该组生物标志物。 基于患者结果，该子集被识别而没有标记。 相反，生物标志物评分（例如，序列评分）用于鉴定生物标志物的子集。

    Modeling lung cancer survival probability after or side-effects from therapy
    4.
    发明授权
    Modeling lung cancer survival probability after or side-effects from therapy 有权
    建立肺癌存活概率或治疗后副作用

    公开(公告)号：US08032308B2

    公开(公告)日：2011-10-04

    申请号：US12399274

    申请日：2009-03-06

    申请人： Shipeng Yu ,  Glenn Fung ,  Cary Dehing-Oberije ,  Dirk de Ruysscher ,  Sriram Krishnan ,  R. Bharat Rao ,  Philippe Lambin

    发明人： Shipeng Yu ,  Glenn Fung ,  Cary Dehing-Oberije ,  Dirk de Ruysscher ,  Sriram Krishnan ,  R. Bharat Rao ,  Philippe Lambin

    IPC分类号： G01N33/48 ,  G01N33/50

    CPC分类号： G06N99/005 ,  A61N5/1031 ,  A61N2005/1041 ,  G06F19/00 ,  G16H50/50

    摘要： Modeling of prognosis of survivability, side-effect, or both is provided. For example, RILI is predicted using bullae information. The amount, volume or ratio of Bullae, even alone, may indicate the likelihood of complication, such as the likelihood of significant (e.g., stage 3) pneumonitis. As another example, RILI is predicted using uptake values of an imaging agent. Standardized uptake from a functional image (e.g., FDG uptake from a positron emission image), alone or in combination with other features, may indicate the likelihood of side-effect. In another example, survivability, such as two-year survivability, is predicted using blood biomarkers. The characteristics of a patient's blood may be measured and, alone or in combination with other features, may indicate the likelihood of survival. The modeling may be for survivability, side-effect, or both and may use one or more of the blood biomarker, uptake value, and bullae features.

    摘要翻译： 提供了对生存能力，副作用或两者的预后的建模。 例如，使用大疱信息预测RILI。 Bullae的数量，体积或比例，甚至单独可能表明并发症的可能性，例如显着（例如阶段3）肺炎的可能性。 作为另一个例子，使用成像剂的摄取值来预测RILI。 来自功能图像的标准摄取（例如，来自正电子发射图像的FDG摄取）单独或与其它特征组合可以指示副作用的可能性。 在另一个例子中，使用血液生物标志物来预测存活能力，例如两年生存能力。 可以测量患者血液的特征，单独或与其它特征组合可能表明存活的可能性。 建模可以是存活性，副作用或两者，并且可以使用一种或多种血液生物标志物，摄取值和大疱特征。

    Systems and Methods for Automated Diagnosis and Decision Support for Breast Imaging
    9.
    发明申请
    Systems and Methods for Automated Diagnosis and Decision Support for Breast Imaging 审中-公开
    乳腺成像自动诊断和决策支持的系统和方法

    公开(公告)号：US20100121178A1

    公开(公告)日：2010-05-13

    申请号：US12621363

    申请日：2009-11-18

    申请人： Sriram Krishnan ,  R. Bharat Rao ,  Murat Dundar ,  Glenn Fung

    发明人： Sriram Krishnan ,  R. Bharat Rao ,  Murat Dundar ,  Glenn Fung

    IPC分类号： A61B5/055 ,  A61B8/14

    CPC分类号： G06T7/0012 ,  G06F19/321 ,  G06T2207/10088 ,  G06T2207/10116 ,  G06T2207/20084 ,  G06T2207/30068 ,  G16H40/63 ,  G16H50/20 ,  Y10S128/92 ,  Y10S128/922 ,  Y10S128/923 ,  Y10S128/924

    摘要： CAD (computer-aided diagnosis) systems and applications for breast imaging are provided, which implement methods to automatically extract and analyze features from a collection of patient information (including image data and/or non-image data) of a subject patient, to provide decision support for various aspects of physician workflow including, for example, automated diagnosis of breast cancer other automated decision support functions that enable decision support for, e.g., screening and staging for breast cancer. The CAD systems implement machine-learning techniques that use a set of training data obtained (learned) from a database of labeled patient cases in one or more relevant clinical domains and/or expert interpretations of such data to enable the CAD systems to “learn” to analyze patient data and make proper diagnostic assessments and decisions for assisting physician workflow.

    摘要翻译： 提供了用于乳腺成像的CAD（计算机辅助诊断）系统和应用，其实现了从受试患者的患者信息（包括图像数据和/或非图像数据）的集合中自动提取和分析特征的方法，以提供 对医生工作流程的各个方面的决策支持，包括例如乳腺癌的自动诊断其他自动化决策支持功能，其能够为乳腺癌的筛选和分期提供决策支持。 CAD系统实施机器学习技术，其使用从一个或多个相关临床领域的标记的患者病例的数据库获得（学习）的一组训练数据和/或对这些数据的专家解释，使得CAD系统能够“学习” 分析患者数据，进行适当的诊断评估和决策，以协助医师的工作流程。

    Systems and methods for automated diagnosis and decision support for breast imaging
    10.
    发明授权
    Systems and methods for automated diagnosis and decision support for breast imaging 有权
    用于乳腺成像自动诊断和决策支持的系统和方法

    公开(公告)号：US07640051B2

    公开(公告)日：2009-12-29

    申请号：US10877129

    申请日：2004-06-25

    申请人： Sriram Krishnan ,  R. Bharat Rao ,  Murat Dundar ,  Glenn Fung

    发明人： Sriram Krishnan ,  R. Bharat Rao ,  Murat Dundar ,  Glenn Fung

    IPC分类号： A61B5/00

    CPC分类号： G06T7/0012 ,  G06F19/00 ,  G06F19/321 ,  G06T2207/10088 ,  G06T2207/10116 ,  G06T2207/20084 ,  G06T2207/30068 ,  G16H40/63 ,  G16H50/20 ,  Y10S128/92 ,  Y10S128/922 ,  Y10S128/923 ,  Y10S128/924

    摘要： CAD (computer-aided diagnosis) systems and applications for breast imaging are provided, which implement methods to automatically extract and analyze features from a collection of patient information (including image data and/or non-image data) of a subject patient, to provide decision support for various aspects of physician workflow including, for example, automated diagnosis of breast cancer other automated decision support functions that enable decision support for, e.g., screening and staging for breast cancer. The CAD systems implement machine-learning techniques that use a set of training data obtained (learned) from a database of labeled patient cases in one or more relevant clinical domains and/or expert interpretations of such data to enable the CAD systems to “learn” to analyze patient data and make proper diagnostic assessments and decisions for assisting physician workflow.

    摘要翻译： 提供了用于乳腺成像的CAD（计算机辅助诊断）系统和应用，其实现了从受试患者的患者信息（包括图像数据和/或非图像数据）的集合中自动提取和分析特征的方法，以提供 对医生工作流程的各个方面的决策支持，包括例如乳腺癌的自动诊断其他自动化决策支持功能，其能够为乳腺癌的筛选和分期提供决策支持。 CAD系统实施机器学习技术，其使用从一个或多个相关临床领域的标记的患者病例的数据库获得（学习）的一组训练数据和/或对这些数据的专家解释，使得CAD系统能够“学习” 分析患者数据，进行适当的诊断评估和决策，以协助医师的工作流程。