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    Systems and methods for treating, diagnosing and predicting the occurrence of a medical condition
    2.
    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。