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    Systems and methods for treating, diagnosing and predicting the occurrence of a medical condition
    3.
    发明申请
    Systems and methods for treating, diagnosing and predicting the occurrence of a medical condition 有权
    用于治疗，诊断和预测医疗状况发生的系统和方法

    公开(公告)号：US20100184093A1

    公开(公告)日：2010-07-22

    申请号：US12584048

    申请日：2009-08-28

    申请人： Michael Donovan ,  Faisal Khan ,  Jason Alter ,  Gerardo Fernandez ,  Ricardo Mesa-Tejada ,  Douglas Powell ,  Valentina Bayer Zubek ,  Stefan Hamann ,  Carlos Cordon-Cardo ,  Jose Costa

    发明人： Michael Donovan ,  Faisal Khan ,  Jason Alter ,  Gerardo Fernandez ,  Ricardo Mesa-Tejada ,  Douglas Powell ,  Valentina Bayer Zubek ,  Stefan Hamann ,  Carlos Cordon-Cardo ,  Jose Costa

    IPC分类号： G01N33/53 ,  C12M1/34

    CPC分类号： G06F19/3437 ,  G06F19/00 ,  G16H50/20 ,  G16H50/50 ,  G16H50/70

    摘要： Clinical information, molecular information and/or computer-generated morphometric information is used in a predictive model for predicting the occurrence of a medical condition. In an embodiment, a model predicts whether a patient is likely to have a favorable pathological stage of prostate cancer, where the model is based on features including one or more (e.g., all) of preoperative PSA, Gleason Score, a measurement of expression of androgen receptor (AR) in epithelial and stromal nuclei and/or a measurement of expression of Ki67-positive epithelial nuclei, a morphometric measurement of a ratio of area of epithelial nuclei outside gland units to area of epithelial nuclei within gland units, and a morphometric measurement of area of epithelial nuclei distributed away from gland units. In some embodiments, quantitative measurements of protein expression in cell lines are utilized to objectively assess assay (e.g., multiplex immunofluorescence (IF)) performance and/or to normalize features for use within a predictive model.

    摘要翻译： 临床信息，分子信息和/或计算机生成的形态学信息用于预测医疗状况发生的预测模型中。 在一个实施方案中，模型预测患者是否可能具有前列腺癌的良好病理分期，其中模型基于包括术前PSA（Gleason Score），Gleason评分（Gleason Score）的一个或多个（例如全部） 雄激素受体（AR）在上皮和基质细胞核中的表达和/或Ki67阳性上皮细胞核的表达测量，腺体单位上皮细胞核的面积与腺体单位内上皮细胞核的比例的形态测量，以及形态学 上皮细胞核的分布远离腺体单位的面积测量。 在一些实施方案中，使用细胞系中的蛋白质表达的定量测量来客观地评估测定（例如，多重免疫荧光（IF））性能和/或归一化在预测模型中使用的特征。

    Systems and methods for treating, diagnosing and predicting the occurrence of a medical condition
    5.
    发明申请
    Systems and methods for treating, diagnosing and predicting the occurrence of a medical condition 有权
    用于治疗，诊断和预测医疗状况发生的系统和方法

    公开(公告)号：US20100177950A1

    公开(公告)日：2010-07-15

    申请号：US12462041

    申请日：2009-07-27

    申请人： Michael Donovan ,  Faisal Khan ,  Gerardo Fernandez ,  Ali Tabesh ,  Ricardo Mesa-Tejada ,  Carlos Cordon-Cardo ,  Jose Costa ,  Stephen Fogarasi ,  Yevgen Vengrenyuk

    发明人： Michael Donovan ,  Faisal Khan ,  Gerardo Fernandez ,  Ali Tabesh ,  Ricardo Mesa-Tejada ,  Carlos Cordon-Cardo ,  Jose Costa ,  Stephen Fogarasi ,  Yevgen Vengrenyuk

    IPC分类号： G06K9/00 ,  G06F19/00

    CPC分类号： G16H50/20 ,  G06F19/00 ,  G16H50/30 ,  G16H50/50 ,  G16H50/70

    摘要： Clinical information, molecular information and/or computer-generated morphometric information is used in a predictive model for predicting the occurrence of a medical condition. In an embodiment, a model predicts risk of prostate cancer progression in a patient, where the model is based on features including one or more (e.g., all) of preoperative PSA, dominant Gleason Grade, Gleason Score, at least one of a measurement of expression of AR in epithelial and stromal nuclei and a measurement of expression of Ki67-positive epithelial nuclei, a morphometric measurement of average edge length in the minimum spanning tree (MST) of epithelial nuclei, and a morphometric measurement of area of non-lumen associated epithelial cells relative to total tumor area. In some embodiments, the morphometric information is based on image analysis of tissue subject to multiplex immunofluorescence and may include characteristic(s) of a minimum spanning tree (MST) and/or a fractal dimension observed in the images.

    摘要翻译： 临床信息，分子信息和/或计算机生成的形态学信息用于预测医疗状况发生的预测模型中。 在一个实施方案中，模型预测患者中前列腺癌进展的风险，其中模型基于包括术前PSA，优势Gleason等级，格里森评分（Gleason Score），格里森评分（Gleason Score）的一个或多个（例如，全部） AR在上皮和基质核中的表达以及Ki67阳性上皮细胞核表达的测量，上皮细胞核的最小生成树（MST）中平均边缘长度的形态测量以及非内腔相关区域的形态测量 上皮细胞相对于总肿瘤面积。 在一些实施方案中，形态学信息基于经多重免疫荧光的组织的图像分析，并且可以包括在图像中观察到的最小生成树（MST）和/或分形维数的特征。

    Systems and methods for predicting disease progression in patients treated with radiotherapy
    8.
    发明申请
    Systems and methods for predicting disease progression in patients treated with radiotherapy 审中-公开
    用放射治疗患者预测疾病进展的系统和方法

    公开(公告)号：US20120010528A1

    公开(公告)日：2012-01-12

    申请号：US13066870

    申请日：2011-04-26

    申请人： Michael Donovan ,  Faisal Khan

    发明人： Michael Donovan ,  Faisal Khan

    IPC分类号： A61B10/02 ,  G06K9/00

    CPC分类号： G06K9/6251 ,  G06K9/00147 ,  G06T7/11 ,  G06T7/187 ,  G06T7/194 ,  G06T7/62 ,  G06T2207/10056 ,  G06T2207/30024

    摘要： Clinical information, molecular information and/or computer-generated morphometric information is used in a predictive model for predicting the occurrence of a medical condition. In an embodiment, a model predicts whether a disease (e.g., prostate cancer) is likely to progress in a patient after radiation therapy. In some embodiments, the molecular and computer-generated morphometric information is obtained through computer analysis of tissue obtained from the patient via a needle biopsy at diagnosis and before treatment of the patent with radiation therapy.

    摘要翻译： 临床信息，分子信息和/或计算机生成的形态学信息用于预测医疗状况发生的预测模型中。 在一个实施方案中，模型预测放射治疗后患者是否可能进展疾病（例如前列腺癌）。 在一些实施方案中，分子和计算机生成的形态学信息通过在诊断时和在用放射疗法处理该专利之前通过针穿刺活检从患者获得的组织的计算机分析获得。