预后预测模型在临床非转移肾细胞癌中的应用价值

  目的  分析比较SSIGN、Leibovich、UISS 3种预后预测模型对临床非转移肾细胞癌患者预后的预测价值。  方法  对1999~2012年间行手术治疗的1 202例临床非转移肾细胞癌患者进行生存分析，并将其临床及病理参数带入SSIGN、Leibovich、UISS 3种肾细胞癌预后预测模型计算其预后危险度。通过计算3种预测模型的ROC曲线下面积(AUC)，AUC在0.50~0.70为低区分度；在0.71~0.90之间为中等区分度；而>0.90则为高区分度。了解3种预测模型对不同分期、分级的临床非转移肾细胞癌的预测强度。  结果  1 202例临床非转移肾细胞癌患者中局限性肾细胞癌1 030例，局部进展期肾细胞癌172例。所有患者中位随访时间为63.02月，其2年及5年总生存率分别为94.7%，87.6%，不同分期、分级患者5年生存率差异有统计学意义(P＜0.05)；UISS、SSIGN及Leibovich评分系统对于局限性肾细胞癌总生存的预测精度分别为0.667、0.785、0.758，而3种模型对于局部进展期肾细胞癌的预测精度均在0.6以下。  结论  3种预测模型中SSIGN预后评分系统在本研究验证中有一定预测价值，但对于进展期肾细胞癌患者，现有预测模型区分价值有限。

Application of Prognostic Predication Model in Clinical Non-metastatic Renal Cell Carcinoma

  Objective   To study and compare the practical use of three prognostic predication models in clinical non-metastatic renal cell carcinoma (RCC).  Methods   We retrospectively collected the data of 1 202 clinical non-metastatic RCC patients operated on between 1999 and 2012 at West China Hospital of Sichuan University. Survival analysis method was used to establish three prognostic prediction models including SSIGN, Leibovich and UISS based on different clinical and pathological indicators. The predictive ability was evaluated by the area under receiver operating characteristic curve (AUC).  Results   Of the 1 202 clinical non-metastatic RCC patients, 1 030 cases were limited RCC patients, and 172 cases were locally advanced RCC patients. The median follow-up time of the patients was 63.02 months. The 2-year and 5-year overall survival rate were 94.7% and 87.6%, respectively. The 5-year overall survival rates of patients with different clinical stages and grades were significantly different. The predictive accuracies for limited RCC of UISS, SSIGN and Leibovich model were 0.667, 0.785 and 0.758, respectively. For locally advanced RCC, the predictive accuracies of the three models were all lower than 0.6.  Conclusion   SSIGN has certain predictive value in clinical non-metastatic RCC. However, for the advanced RCC, all the prognostic models demonstrate limited predictive value.