节点文献
胃癌患者临床与病理TNM分期一致性分析
Analysis of Consistency between Clinical and Pathological TNM Staging for Gastric Cancer
【作者】 李靖；
【导师】 高鹏；
【作者基本信息】 兰州大学 ， 外科学， 2013， 硕士
【摘要】 目的：探讨患者术前TNM分期与术后病理TNM分期的一致性并分析其原因。方法：选取甘肃省人民医院普外科2010年12月至2011年12月期间手术治疗的胃癌患者临床资料完整者64例,参照日本胃癌学会(JGCA)(2010年第14版)拟定的胃癌TNM分期标准,对所有患者进行术后病理分期,并与术前临床分期进行对比。利用统计工具SPSS18.0,通过T分期,N分期与M分期统计量的一致性分析,比较两种分期的符合率。结果：临床与术后病理T分期的Kappa值为0.729(0.4-0.75),两者一致性较好,但cT2组符合率最低,有显著性差异(P<0.05)。临床与术后病理N分期的Kappa值为0.216(0.4-0.75),两者一致性较差,其中cN3组的符合率最低,有显著性差异(P<0.05)。临床与术后病理M分期的Kappa值为0.743(0.4-0.75),两者一致性较好。结论：新版《胃癌处理规约》应用临床后,可以较为准确的判断TNM分期,有利于指导个体化的治疗方案,但其中仍存在高估或者低估的情况,尤其是N分期的差异性较大,因此,术前综合各种资料,正确判断N分期是提高术前TNM分期准确率,制定治疗方案的关键。
【Abstract】 Objective To explore the consistency between preoperative clinical TNM stage and postoperative pathology TNM stage of gastric cancer and look for the under reason.Methods64patients of gastric cancer with complete clinical data from December2010to December2011in Gansu provincial hospital were enrolled into this study. All cases were performed postoperative pathologic TNM stage according to the TNM stage criterion of gastric cancer from JGCA(the14th edition,2010), and the results were compared with preoperative TNM stage, then we studied the coincidence rate of both staging by analysis of the consistency of T staging, N staging and M staging using SPSS18.0.Results The Kappa value from T staging of preoperative and postoperative is0.729, it shows us a good consistency, but the coincidence of cT2group is the lowest(P<0.05).The N stage’s consistency is lower, Kappa value is only0.216, among them, the coincidence of cN3group is the lowest(P<0.05).For M staging, Kappa value is0.743,they have a good consistency.Conclusions According to the General Rules for Gastric Cancer Treatment, the author can make a accurate TNM stage and individual treatment plan for gastric cancer patients in clinic. But the overestimate and underestimate still existed, especially in N staging. Therefore, make a accurate judge for N staging according to all kinds of preoperative clinical data and images is the key for improvement of preoperative TNM staging accuracy and treatment.