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出 处:《天津医科大学学报》2005年第4期590-595,共6页Journal of Tianjin Medical University
摘 要:目的:评价乳腺癌患者预后状况,单一预后相关因素解释常常会产生矛盾。多个预后相关的临床、病理因素建立乳腺癌综合预后指标即预后指数,能更准确地评价患者的预后,为个体化治疗提供帮助。方法:回顾性分析1989年1月~1994年6月间,天津医科大学肿瘤医院外科治疗、病理证实、资料完整可供研究且随访10年的病例共1496例。采用COX模型对预后相关因素进行单因素、多因素分析,建立10年预后生存、健在指数公式,并给予验证。结果:预后指数公式分别为:1.生存预后指数公式=0.40×年龄+0.51×皮肤改变+0.11×肿块大小+0.06×淋巴结转移数+0.34×病理分类;健在预后指数公式=0.54×年龄+0.58×皮肤改变+0.12×肿块大小+0.06×淋巴结转移数-0.49×病理分类+0.45×软组织阳性。2.预后指数的预测符合率分别为:10年生存、健在预测符合率为77.41%、73.99%。结论:用天津医科大学肿瘤医院资料建立预后指数公式,经验证明预后公式能较好地判断患者预后状况,对临床治疗方案的制定具有一定指导意义。Objective: The research for single prognostic factor in breast cancer has often produced conflicting. Therefore, prognostic index, which has been compiled by combining several parameters could assess prognosis more accurately. Methods: A total of 1 496 women with histological proven primary invasive breast cancer who had been treated with mastectomy and axillary lymph node dissection at Tianjin Cancer Hospital in Tianjin Medical University between 1989 and 1994 and had been selected for our studies had been followed up for 10 years. Their data were retrospectively reviewed. Set up overall survival prognostic index models and disease-free survival prognostic index models based on the groups of total patients. Univariate analysis(Cox model) and multivariate analysis (Cox model) were used to get prognostic indexes. Results: 1.The overall survival prognostic index model =0.4×age+0.51xskin+ 0.11×tumor size+0.06× number of node metastsis+0.34×histological type;The disease-free survival prognostic index model =0.54×age+0.57×skin+0.12×tumor size+0.06×number of node metastsis-0.49×histological type +0.45×extra capsular extension. 2.Rate of predictive coincidence of 10-year overall survival was 77.41% .Rate of predictive coincidence of 10-year disease-free survival was 73.99%. Conclusion: Prognostic index models based on the data from Tianjin Cancer Hospital in Tianjin Medical University were built in this study. They did well in assessing patient's prognosis with primary invasive breast cancer. They are useful to choose appropriate treatments.
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