| 卵巢浆液性腺癌预后评分模型 |
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| 引用本文： | 胡君,朱丽荣,廖秦平,高雨农,郑虹. 卵巢浆液性腺癌预后评分模型[J]. 肿瘤, 2010, 30(2). DOI: 10.3781/j.issn.1000-7431.2010.02.008 |
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| 作者姓名： | 胡君 朱丽荣 廖秦平 高雨农 郑虹 |
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| 作者单位： | 1. 北京大学第一医院妇产科,北京,100034
2. 北京肿瘤医院妇科,北京,100142 |
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| 摘 要： | 目的:分析卵巢浆液性腺癌的预后相关因素,建立量化的预后评分模型并检验该模型的效用.方法:分别收集1995年1月-2003年12月卵巢浆液性腺癌训练样本(181例)以及1999年1月-2005年12月卵巢浆液性腺癌检验样本(42例)的临床资料进行回顾性分析.应用Kaplan-Meier单因素生存分析筛选预后相关因素,COX单因素和多因素回归分析确定各分层因素的系数,Pearson等级相关分析了解各因素之间的相关性,将风险系数转换为评分,建立卵巢浆液性腺癌预后评分模型.应用受试者工作特征(receiver operating characteristic, ROC)曲线分析判断界值,结合患者实际的3年生存情况和Ki67值,检验预后评分模型的敏感度和特异度.结果: 卵巢浆液性腺癌患者的生存率与临床分期、病理分级、手术残余病灶大小、淋巴结转移情况、术后化疗情况和术前血清CA125水平等6项因素相关,其中术后化疗是相对独立的预后因素.该预后评分模型能够直观地反映COX风险比例模型的生存率.将预后评分与Ki67值进行联合判断时,其敏感度和特异度分别可达64.7%和96.0%.结论:临床分期、病理分级、手术残余病灶大小、淋巴结转移情况、术后化疗情况和术前血清标志物CA125水平是影响卵巢浆液性腺癌患者预后的主要因素.卵巢浆液性腺癌预后评分模型能够较好地反映患者的实际生存情况,预后评分与Ki67值进行联合判断可以显著提高该模型的敏感度和特异度.
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| 关 键 词： | 卵巢肿瘤 腺癌 浆液性 预后 模型 统计学 |
Construction and verification of the prognostic model of ovarian serous adenocarcinoma |
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| HU Jun,ZHU Li-rong,LIAO Qin-ping,GAO Yu-nong,ZHENG Hong. Construction and verification of the prognostic model of ovarian serous adenocarcinoma[J]. Tumor, 2010, 30(2). DOI: 10.3781/j.issn.1000-7431.2010.02.008 |
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| Authors: | HU Jun ZHU Li-rong LIAO Qin-ping GAO Yu-nong ZHENG Hong |
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| Affiliation: | HU Jun1,ZHU Li-rong1,LIAO Qin-ping1,GAO Yu-nong2,ZHENG Hong2 (1. Department of Obstetrics , Gynecology,Peking University First Hospital,Beijing 100034,China,2. Department of Gynecologic Oncology,Beijing Cancer Hospital,Beijing 100142,China) |
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| Abstract: | Objective:To analyze the prognosis-related factors of ovarian serous adenocarcinoma in order to set up a prognostic model of serous adenocarcinoma and verify the effectiveness of the model as prognostic clinical criteria.Methods:The clinical,patholo-gical and follow-up data from 181 training samples with ovarian serous adenocarcinoma in Peking University First Hospital during January 1995 to December 2003 and another 42 detection samples with ovarian serous adenocarcinoma in Beijing Cancer Hospital during J... |
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| Keywords: | Ovarian neoplasms Adenocarcinoma serous Prognosis Models statistical |
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