Objective: To establish a nomogram prognostic model for predicting the 5-, 10-, and 15-year overall survival (OS) of non-metastatic renal cell carcinoma patients managed with radical nephrectomy (RN), compare the modelled results with the results of pure pathologic staging, the Karakiewicz nomogram and the Mayo Clinic Stage, Size, Grade, and Necrosis (SSIGN) score commonly used in foreign countries, and stratify the patients into different prognostic risk subgroups. Methods: A total of 1 246 non-metastatic renal cell carcinoma patients managed with RN in Sun Yat-sen University Cancer Center (SYSUCC) from 1999 to 2020 were retrospectively analyzed. Multivariate Cox regression analysis was used to screen the variables that influence the prognosis for nomogram establishment, and the bootstrap random sampling was used for internal validation. The time-receiver operating characteristic curve (ROC), the calibration curve and the clinical decision curve analysis (DCA) were applied to evaluate the nomogram. The prediction efficacy of the nomogram and that of the pure pathologic staging, the Karakiewicz nomogram and the SSIGN score was compared through the area under the curve (AUC). Finally, patients were stratified into different risk subgroups according to our nomogram scores. Results: A total of 1 246 patients managed with RN were enrolled in this study. Multivariate Cox regression analysis showed that age, smoking history, pathological nuclear grade, sarcomatoid differentiation, tumor necrosis and pathological T and N stages were independent prognostic factors for RN patients (all P<0.05). A nomogram model named SYSUCC based on these factors was built to predict the 5-, 10-, and 15-year survival rate of the participating patients. In the bootstrap random sampling with 1 000 iterations, all these factors occurred for more than 800 times as independent predictors. The Harrell's concordance index (C-index) of SYSUCC was higher compared with pure pathological staging [0.770 (95% CI: 0.716-0.823) vs 0.674 (95% CI: 0.621-0.728)]. The calibration curve showed that the survival rate as predicted by the SYSUCC model simulated the actual rate, while the clinical DCA showed that the SYSUCC nomogram has a benefit in certain probability ranges. In the ROC analysis that included 857 patients with detailed pathological nuclear stages, the nomogram had a larger AUC (5-/10-year AUC: 0.823/0.804) and better discriminating ability than pure pathological staging (5-/10-year AUC: 0.701/0.658), Karakiewicz nomogram (5-/10-year AUC: 0.772/0.734) and SSIGN score (5-/10-year AUC: 0.792/0.750) in predicting the 5-/10-year OS of RN patients (all P<0.05). In addition, the AUC of the SYSUCC nomogram for predicting the 15-year OS (0.820) was larger than that of the SSIGN score (0.709), and there was no statistical difference (P<0.05) between the SYSUCC nomogram, pure pathological staging (0.773) and the Karakiewicz nomogram (0.826). The calibration curve was close to the standard curve, which indicated that the model has good predictive performance. Finally, patients were stratified into low-, intermediate-, and high-risk subgroups (738, 379 and 129, respectively) according to the SYSUCC nomogram scores, among whom patients in intermediate- and high-risk subgroups had a worse OS than patients in the low-risk subgroup (intermediate-risk group vs. low-risk group: HR=4.33, 95% CI: 3.22-5.81, P<0.001; high-risk group vs low-risk group: HR=11.95, 95% CI: 8.29-17.24, P<0.001), and the high-risk subgroup had a worse OS than the intermediate-risk group (HR=2.63, 95% CI: 1.88-3.68, P<0.001). Conclusions: Age, smoking history, pathological nuclear grade, sarcomatoid differentiation, tumor necrosis and pathological stage were independent prognostic factors for non-metastasis renal cell carcinoma patients after RN. The SYSUCC nomogram based on these independent prognostic factors can better predict the 5-, 10-, and 15-year OS than pure pathological staging, the Karakiewicz nomogram and the SSIGN score of patients after RN. In addition, the SYSUCC nomogram has good discrimination, agreement, risk stratification and clinical application potential.
目的： 构建一个预测非转移性肾癌根治性肾切除患者术后总生存的列线图模型，并与单纯病理分期和国外常用的Karakiewicz列线图模型和梅奥SSIGN(Stage, Size, Grade, and Necrosis)评分进行比较，最后对患者进行预后分层。 方法： 收集1999—2020年中山大学肿瘤防治中心行根治性肾切除的肾癌患者1 246例，采用多因素Cox回归分析筛选预后影响因素，构建预测非转移性肾癌根治性肾切除患者术后总生存的列线图模型，并采用bootstrap抽样进行内部验证，采用时间受试者工作特征(ROC)曲线、校准曲线和临床决策曲线对模型进行评价，采用ROC曲线分析比较该模型与Karakiewicz列线图模型、SSIGN预后评分系统、病理分期的预测效能。根据患者的列线图模型得分进行危险分层。 结果： 多因素Cox回归分析显示，年龄、吸烟史、病理核分级、肉瘤样分化、肿瘤坏死、病理T分期和病理N分期是根治性肾切除术患者总生存的独立影响因素(均P<0.05)。构建预测非转移性肾癌根治性肾切除术患者5、10和15年生存率的中山大学肿瘤防治中心(SYSUCC)列线图模型。bootstrap抽样1 000次显示，SYSUCC列线图模型相关因素被纳入多因素Cox回归模型的次数均>800次。SYSUCC列线图模型的C指数为0.770(95% CI：0.716~0.823)，高于单纯病理分期[0.674(95% CI：0.621~0.728)]。校准曲线显示，SYSUCC列线图模型预测的生存率与实际生存率相仿。临床决策曲线显示，在一定概率阈值范围内，SYSUCC列线图模型具有净获益。基于857例有详细病理核分级资料的患者的ROC曲线分析显示，SYSUCC列线图模型预测5年和10年总生存的曲线下面积(分别为0.823和0.804)均高于Karakiewicz列线图模型、SSIGN预后评分系统和病理分期(均P<0.05)，预测15年总生存的曲线下面积(分别为0.820)高于SSIGN预后评分系统(P<0.05)，与Karakiewicz列线图模型和病理分期差异无统计学意义(P>0.05)。通过SYSUCC列线图模型可以将1 246例患者分为低危组(738例)、中危组(379例)和高危组(129例)，中危组、高危组患者较低危组患者总生存更差(中危组：HR＝4.33，95% CI：3.22~5.81；高危组：HR＝11.95，95% CI：8.29~17.24)，高危组患者较中危组患者总生存更差(HR＝2.63，95% CI：1.88~3.68)。 结论： 根据非转移性肾癌根治性肾切除术患者独立预后因素(年龄、吸烟史、病理核分级、肉瘤样分化、肿瘤坏死、病理T分期和病理N分期)构建的SYSUCC列线图模型区分度好、一致性强、准确度高，能很好地预测非转移性肾癌根治性肾切除患者的术后总生存，并能对根治性肾切除患者进行危险分层，具有较好的临床应用前景。.
Keywords: Nomogram; Prognosis; Radical nephrectomy; Renal neoplasms.