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The Clinical Study On Prognostic Factors Of Epithelial Ovarian Cancer

Posted on:2018-08-11Degree:MasterType:Thesis
Country:ChinaCandidate:H WuFull Text:PDF
GTID:2334330515458905Subject:Obstetrics and gynecology
Abstract/Summary:PDF Full Text Request
Objective:To select out independent factors through discussion and analysis on the influence of different factors on the prognosis of the epithelial ovarian cancer(EOC).And to make a survival prediction assessment system by combining the scores with survival rates.Methods:We collected 99 cases of clinical data of patients with EOC which accepted treatment in People's Hospital from January,2008 to January,2012 and use Kaplan-Meier estimator to analyze the data in SPSS.By Cox's proportional hazards regression model,we compared the influence of various factors on the prognosis and got the HRs.We turned the HRs to scores and make a survival prediction assessment system.Result:FIGO stage,pathological type,histologic grade and lymph node situation affected the prognosis of patients(P<0.05).But Cox Multivariate analysis showed,FIGO stage(HR=2.605,P<0.05,95%CI=1.800-3.769).histologic grade(HR=1.865,P=0.01,95%CI=1.273-2.732).and lymph node situation(HRF=1.221,P<0.05,95%CI=0.794-1.880)are independent factors on the prognosis of patients with EOC.Here was the survival prediction assessment system:score<125s it predicts that the 5-year survival rate is 74.2%;when the score between 125 and 200,it predicts that the 5-year survival rate is 40%;and when score>200,it predicts that the 5-year survival rate is 2.3%.Conclusion:FIGO stage.histologic grade and lymph node situation are the independent factors on the prognosis of patients with EOC.In this study,the influence of the FIGO stage on the prognosis was the most important.The survival prediction assessment system can predict epithelial ovarian cancer survival probability.The higher score the patient gets,the more probably she will die.
Keywords/Search Tags:epithelial ovarian cancer, 5-years survival rate, prognosis, Cox's proportional hazards regression mode, the survival prediction assessment system
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