Construction and utilization of the prognostic model of serous ovarian adenocarcinoma / 中华妇产科杂志

ABSTRACT

Objective To analyze the related factors with prognosis in patients with serous ovarian adenocarcinoma and to set up a prognostic model of serous ovarian adenocarcinoma.Methods The clinical, pathological and follow-up data of 104 cases with serous ovarian adenocarcinoma were retrospectively analyzed.Kaplan-meier univariate analysis was used to screen the prognostic factors;COX univariate and multivariate analyses were used to determine the risk coefficient of each factors and different layers in each factor.Pearson rank correlation was used to reject the influence of different factors with each other.And the prognostic model of serous ovarian adenocarcinoma was set up based on the result of the above study,which could be used to deduce the survival probability of patients with serous ovarian adenocarcinoma.Results International Federation of Gynecology and Obstetrics(FIGO)stage(P=0.0029),histological grade(P= 0.0054),residual disease(P=0.0000),metastasis of lymph nodes(P=0.0000)and chemotherapy(P= 0.0000)were the related factors of prognosis in patients with serous ovarian adenocareinoma,of which FIGO stage was the most important one,followed sequentially by histological grade,metastasis of lymph node, residual disease and chemotherapy(the independent risk coefficient of each factor was 1.3392,0.9206, 0.7071,0.6004,0.4985 in sequence).We set up a prognosis model according to the prognostic index of each factors.The effect of chemotherapy and residual disease on prognosis could be quantified by this model, and the higher the score,the lower the survival probability of patients.Condusions FIGO stage, histological grade,residual disease,metastasis of lymph nodes and chemotherapy are important prognostic factors of serous ovarian adenoearcinoma.This model can be used to estimate the prognosis of patients with serous ovarian adenoearcinoma,and the effect of both chemotherapy and residual disease on the prognosis could be quantified by the model.