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    Modeling lung cancer survival probability after or side-effects from therapy
    3.
    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。

    MODELING LUNG CANCER SURVIVAL PROBABILITY AFTER OR SIDE-EFFECTS FROM THERAPY
    5.
    发明申请
    MODELING LUNG CANCER SURVIVAL PROBABILITY AFTER OR SIDE-EFFECTS FROM THERAPY 有权
    建立肺癌患者治疗后或治疗效果后的存活率

    公开(公告)号：US20090234627A1

    公开(公告)日：2009-09-17

    申请号：US12399274

    申请日：2009-03-06

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

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

    IPC分类号： G06G7/60

    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。

    Knowledge-Based Interpretable Predictive Model for Survival Analysis
    7.
    发明申请
    Knowledge-Based Interpretable Predictive Model for Survival Analysis 有权
    基于知识的解释性生存分析预测模型

    公开(公告)号：US20100057651A1

    公开(公告)日：2010-03-04

    申请号：US12506583

    申请日：2009-07-21

    申请人： Glenn Fung ,  Cary Dehing-Oberije ,  Andreas Lubbertus Aloysius Johannes Dekker ,  Philippe Lambin ,  Shipeng Yu ,  Kartik Jayasurya Komati

    发明人： Glenn Fung ,  Cary Dehing-Oberije ,  Andreas Lubbertus Aloysius Johannes Dekker ,  Philippe Lambin ,  Shipeng Yu ,  Kartik Jayasurya Komati

    IPC分类号： G06F15/18 ,  G06N5/02

    CPC分类号： G06N7/005 ,  A61N2005/1041

    摘要： Knowledge-based interpretable predictive modeling is provided. Expert knowledge is used to seed training of a model by a machine. The expert knowledge may be incorporated as diagram information, which relates known causal relationships between predictive variables. A predictive model is trained. In one embodiment, the model operates even with a missing value for one or more variables by using the relationship between variables. For application, the model outputs a prediction, such as the likelihood of survival for two years of a lung cancer patient. A graphical representation of the model is also output. The graphical representation shows the variables and relationships between variables used to determine the prediction. The graphical representation is interpretable by a physician or other to assist in understanding.

    摘要翻译： 提供基于知识的可解释预测模型。 专家知识用于通过机器对模型进行种子培训。 专家知识可以作为图表信息并入，其涉及预测变量之间的已知因果关系。 一个预测模型被训练。 在一个实施例中，通过使用变量之间的关系，该模型甚至通过一个或多个变量的缺失值来运行。 为了应用，该模型输出预测，例如两年的肺癌患者的生存可能性。 还会输出模型的图形表示。 图形表示显示用于确定预测的变量和变量之间的关系。 图形表示可由医生或其他人解释，以协助理解。

    Knowledge-based interpretable predictive model for survival analysis
    8.
    发明授权
    Knowledge-based interpretable predictive model for survival analysis 有权
    基于知识的可解释预测模型进行生存分析

    公开(公告)号：US08078554B2

    公开(公告)日：2011-12-13

    申请号：US12506583

    申请日：2009-07-21

    申请人： Glenn Fung ,  Cary Dehing-Oberije ,  Andreas Lubbertus Aloysius Johannes Dekker ,  Philippe Lambin ,  Shipeng Yu ,  Kartik Jayasurya Komati

    发明人： Glenn Fung ,  Cary Dehing-Oberije ,  Andreas Lubbertus Aloysius Johannes Dekker ,  Philippe Lambin ,  Shipeng Yu ,  Kartik Jayasurya Komati

    IPC分类号： G06N5/00

    CPC分类号： G06N7/005 ,  A61N2005/1041

    摘要： Knowledge-based interpretable predictive modeling is provided. Expert knowledge is used to seed training of a model by a machine. The expert knowledge may be incorporated as diagram information, which relates known causal relationships between predictive variables. A predictive model is trained. In one embodiment, the model operates even with a missing value for one or more variables by using the relationship between variables. For application, the model outputs a prediction, such as the likelihood of survival for two years of a lung cancer patient. A graphical representation of the model is also output. The graphical representation shows the variables and relationships between variables used to determine the prediction. The graphical representation is interpretable by a physician or other to assist in understanding.

    摘要翻译： 提供基于知识的可解释预测模型。 专家知识用于通过机器对模型进行种子培训。 专家知识可以作为图表信息并入，其涉及预测变量之间的已知因果关系。 一个预测模型被训练。 在一个实施例中，通过使用变量之间的关系，该模型甚至通过一个或多个变量的缺失值来运行。 为了应用，该模型输出预测，例如两年的肺癌患者的生存可能性。 还会输出模型的图形表示。 图形表示显示用于确定预测的变量和变量之间的关系。 图形表示可由医生或其他人解释，以协助理解。

    Prognosis Modeling From One or More Sources of Information
    9.
    发明申请
    Prognosis Modeling From One or More Sources of Information 有权
    从一个或多个信息来源的预测建模

    公开(公告)号：US20080033894A1

    公开(公告)日：2008-02-07

    申请号：US11735736

    申请日：2007-04-16

    申请人： Harald Steck ,  Sriram Krishnan ,  R. Rao ,  Philippe Lambin ,  Cary Dehing-Oberije

    发明人： Harald Steck ,  Sriram Krishnan ,  R. Rao ,  Philippe Lambin ,  Cary Dehing-Oberije

    IPC分类号： G06F15/18

    CPC分类号： G16H50/20 ,  G06F19/00 ,  G16H50/70

    摘要： A predictor of medical treatment outcome is developed and applied. A prognosis model is developed from literature. The model is determined by reverse engineering the literature reported quantities. A relationship of a given variable to a treatment outcome is derived from the literature. A processor may then use individual patient values for one or more variables to predict outcome. The accuracy may be increased by including a data driven model in combination with the literature driven model.

    摘要翻译： 开发并应用了治疗结果的预测因子。 从文献开发预后模型。 该模型通过逆向工程文献报道的量来确定。 给定变量与治疗结果的关系来源于文献。 然后，处理器可以使用单个患者值用于一个或多个变量来预测结果。 通过将数据驱动模型与文献驱动模型相结合，可以提高精度。