公开(公告)号：US20130080134A1

公开(公告)日：2013-03-28

申请号：US13488176

申请日：2012-06-04

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

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

IPC分类号： G06F19/00

CPC分类号： G06N7/005 ,  G06F16/2455 ,  G16H50/20 ,  G16H50/50

摘要： In general, one aspect of the subject matter described in this specification can be embodied in methods for assessing risk associated with prostate cancer, the methods including the actions of receiving patient data, comparing, with a processor executing code, the patient data to one or more predictive models, the one or more predictive models comprising at least one of (a) a disease progression (DP) model, the DP model being configured to predicts a likelihood of developing significant disease progression, and (b) a favorable pathology (FP) model, the FP model being configured to predict a likelihood of having organ confined, low grade disease in a prostatectomy, and outputting one or more results of the comparison Other embodiments of the various aspects include corresponding systems, apparatus, and computer program products.

摘要翻译： 通常，本说明书中描述的主题的一个方面可以体现在用于评估与前列腺癌相关的风险的方法中，所述方法包括接收患者数据的动作，将执行代码的处理器与患者数据进行比较，将患者数据与一个或 所述一个或多个预测模型包括以下中的至少一个：（a）疾病进展（DP）模型，所述DP模型被配置为预测发展显着疾病进展的可能性，和（b）有利的病理学 ）模型，FP模型被配置为预测在前列腺切除术中具有器官限制，低等级疾病的可能性，以及输出比较的一个或多个结果。各个方面的其他实施例包括相应的系统，装置和计算机程序产品。

公开(公告)号：US09779213B2

公开(公告)日：2017-10-03

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： G06G7/48 ,  G06F19/00

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.

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。