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中华腔镜泌尿外科杂志(电子版)

中华腔镜泌尿外科杂志(电子版) ›› 2023, Vol. 17 ›› Issue (04) : 343 -350. doi: 10.3877/cma.j.issn.1674-3253.2023.04.007

临床研究

乳头状肾细胞癌预后预测模型的构建及验证
张磊磊, 蒋方, 徐疾飞, 周真文, 郭志文, 毕满华†()   
  1. 246003　安徽医科大学附属安庆第一人民医院泌尿外科
  • 收稿日期:2022-10-28 出版日期:2023-08-01
  • 通信作者: 毕满华

Construction and validation of prognosis prediction model for papillary renal cell carcinoma

Leilei Zhang, Fang Jiang, Jifei Xu, Zhenwen Zhou, Zhiwen Guo, Manhua Bi†()   

  1. Department of Urology, Anqing First People's Hospital of Anhui Medical University, Anqing 246003, China
  • Received:2022-10-28 Published:2023-08-01
  • Corresponding author: Manhua Bi
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引用本文：

张磊磊, 蒋方, 徐疾飞, 周真文, 郭志文, 毕满华. 乳头状肾细胞癌预后预测模型的构建及验证[J]. 中华腔镜泌尿外科杂志(电子版), 2023, 17(04): 343-350.

Leilei Zhang, Fang Jiang, Jifei Xu, Zhenwen Zhou, Zhiwen Guo, Manhua Bi. Construction and validation of prognosis prediction model for papillary renal cell carcinoma[J]. Chinese Journal of Endourology(Electronic Edition), 2023, 17(04): 343-350.

目的

构建乳头状肾细胞癌患者的预后预测模型并进行验证，为该疾病患者的治疗和预后预测提供参考。

方法

从SEER数据库（美国国立癌症研究数据库） （2005~2015年）中收集筛选出共计11 396例乳头状肾细胞癌患者的数据，使用单因素Cox回归分析预后的风险因素，将具有统计学意义的风险因素纳入多因素Cox回归分析其预后的独立影响因素。依据Cox回归结果绘制列线图预测模型，同时将列线图总分值作为风险分层依据进行统计分析。使用C指数（C-index）和校正曲线确定列线图的预测准确度，使用ROC曲线下面积即AUC值判断该模型的区分度。

结果

成功构建了基于年龄、Fuhrman分级、TNM分期、肿瘤大小、手术方式等因素的乳头状肾细胞癌预后预测模型，C指数以及校准曲线验证表明，该列线图模型的预测结果与实际观测结果吻合较好。所有ROC曲线的AUC值均大于0.7，模型建立组和验证组的校准图显示OS和CSS的预测列线图之间具有良好的一致性。

结论

多因素Cox回归分析表明，年龄≥65岁、Fuhrman Ⅲ及Ⅳ级、T3及T4分期、N1及N2分期、M1分期、肿瘤大小≥40 mm、未手术均是影响乳头状肾细胞癌预后的独立危险因素。基于以上参数建立的预后预测模型，能够较为准确的预测乳头状肾细胞癌的预后风险，为临床诊疗提供统计学证据。

关键词: 乳头状肾细胞癌, 预后风险模型, 列线图
Objective

Constructing and validating of the prognositic prediction model of patients with papillary renal cell carcinoma (pRCC), which can provide a reference for the treatment and prognositic prediction of patients with pRCC.

Methods

A total of 11 396 patients with pRCC were selected from SEER database (2005-2015). Univariate Cox regression was used to analyze the risk factors of prognosis, and multivariate Cox regression was used to analyze the independent factors of prognosis. Based on the results of Cox regression, a nomogram colum line chart prediction model would be constructed, and the total score of the nomogram would be used as the basis for risk stratification in siatistical analysis. The c-index and correction curve were used to determine the prediction accuracy of nomogram, and the area under the ROC curve (AUC) was used to judge the discrimination of the model.

Results

A prognostic risk prediction model based on age, Fuhrman grade, TNM stage, tumor size and surgical procedure was successfully established. The c-index and calibration curve fully verified that the prediction results of the nomogram model were in good agreement with the actual observation results. In all ROC curves, all AUC values were greater than 0.7. The calibration plots of the model establishment group and the validation group showed good consistency between the predicted nomograms of overall survival and cancer specific survival.

Conclusions

The multivariate Cox regression analysis showed that age ≥65, Fuhrman Ⅲ and Ⅳ grade, T3 and T4 stage, N1 and N2 stage, M1 stage, tumor size ≥40 mm and unoperated were independent risk facters affecting prognostic of pRCC. Prognostic predictien model based on above parameters has ability to predict the prognosis of pRCC patients, and can be used in clinical work.

Key words: Papillary renal cell carcinoma, Prognostic risk model, Nomogram
图1 乳头状肾细胞癌（pRCC）患者年龄及肿瘤大小数据分割示意图
表1 乳头状肾细胞癌（pRCC）患者临床基线资料
项目 总体数据[例(%)]n=11 396 建模组[例(%)]n=7 977 验证组[例(%)]n=3 419 P值
年龄  
< 65岁 6357 (55.8) 4 433(55.7) 1 914(56.0) 0.738
≥65岁 5039 (44.2) 3 534(44.3) 1 505(44.0)
人种  
白人 7 796(68.4) 5 449(68.3) 2 347(68.6) 0.855
黑人 3 226(28.3) 2 269(28.4) 0957(28.0)
其它 .374(3.3) 259(3.2) 115(3.4)
性别  
男 8 733(76.6) 6100(76.5) 2 633(77.0) 0.532
女 2 663(23.4) 1877(23.5) 786(23.0)
Fuhrman分级  
Ⅰ 1 403(12.3) 0.963(12.1) 440(12.9) 0.136
Ⅱ 6 004(52.7) 4 169(52.3) 1 835(53.7)
Ⅲ 3 535(31.9) 2 595(32.5) 1 040(30.4)
Ⅳ 354(3.3) 250(3.1) 104(3.0)
肿瘤位置  
左肾 5 679(49.8) 3 981(49.9) 1 698(49.7) 0.813
右肾 5 717(50.2) 3 996(50.1) 1 721(50.3)
T分期  
T1 8 875(77.9) 6 206(77.8) 2 669(78.1) 0.915
T2 1 270(11.1) 0.896(11.2) 0.374(10.9)
T3 1 209(10.6) 0.844(10.6) 0.365(10.7)
T4 042(0.4) 0.31(0.4) 0.11(0.3)
N分期  
N0 10 999(96.5)0 7 707(96.6) 3 292(96.3) 0.494
N1 220(1.9) 146(1.8) 0.74(2.2)
N2 177(1.6) 124(1.6) 0.53(1.6)
M分期  
M0 11 133(97.7)0 7 789 (97.6) 3 344(97.8) 0.595
M1 263(2.3) 188 (2.4) 0.75(2.2)
肿瘤大小(mm)  
<40 6 194(54.4) 4 303(53.9) 1 891(55.3) 0.335
40~77 3 705(32.5) 2 626(32.9) 1 079(31.6)
>77 1 497(13.1) 1 048(13.2) 449(13.1)
手术方式  
未手术 213(1.9) 138(1.7) 0.75(2.2) 0.375
部分切除 5 025(44.1) 3 512(44.0) 1 513(44.3)
单纯切除 922(8.1) 0659(8.3) 263(7.7)
根治切除 4 826(42.3) 3 387(42.5) 1 439(42.1)
其它方式 410(3.6) 0281(3.5) 129(3.8)
表2 pRCC患者OS和CSS单因素及多因素Cox回归分析结果
项目 总生存率（OS） pRCC特异性生存率（CSS）
单因素回归分析 多因素回归分析 单因素回归分析 多因素回归分析
HR(95%CI) P值 HR(95%CI) P值 HR(95%CI) P值 HR(95%CI) P值
年龄  
< 65岁 1.00(参考值) 1.00(参考值)
≥65岁 2.267(2.073~2.479) <0.001 2.220(2.028~2.430) <0.001 1.845(1.604~2.122) <0.001 1.806(1.567~2.082) <0.001
人种  
白人 1.00(参考值) 1.00(参考值)
黑人 0.999(0.906~1.102) 0.987 / 0.877(0.747~1.031) 0.112 /
其他 0.885(0.679~1.153) 0.366 / 1.381(0.986~1.934) 0.061 /
性别  
男 1.00(参考值) 1.00(参考值)
女 0.862(0.775~0.960) 0.007 0.810(0.728~0.903) <0.001 0.873(0.737~1.035) 0.117 /
Fuhrman分级  
Ⅰ 1.00(参考值) 1.00(参考值)
Ⅱ 1.079(0.931~1.252) 0.313 1.102(0.948~1.280) 0.206 1.298(0.973~1.731) 0.076 1.260(0.941~1.687) 0.121
Ⅲ 1.517(1.303~1.766) <0.001 1.284(1.096~1.504) 0.002 2.796(2.105~3.714) <0.001 1.834(1.363~2.468) <0.001
Ⅳ 3.403(2.734~4.236) <0.001 1.807(1.428~2.285) <0.001 08.997(6.436~12.578) <0.001 2.549(1.770~3.670) <0.001
肿瘤位置  
左肾 1.00(参考值) 1.00(参考值)
右肾 0.932(0.854~1.017) 0.113 / 0.845(0.736~0.971) 0.017 0.889(0.774~1.023) 0.100
T分期  
T1 1.00(参考值) 1.00(参考值)
T2 1.563(1.374~1.778) <0.001 0.752(0.613~0.923) 0.006 3.067(2.536~3.708) <0.001 1.057(0.797~1.402) 0.702
T3 2.741(2.447~3.071) <0.001 1.295(1.112~1.507) 0.001 6.722(5.737~7.875) <0.001 2.058(1.643~2.578) <0.001
T4 10.855(7.377~16.059) <0.001 1.870(1.229~2.846) 0.003 030.578(19.852~47.100) <0.001 2.761(1.700~4.485) <0.001
N分期  
N0 1.00(参考值) 1.00(参考值)
N1 6.572(5.370~8.042) <0.001 2.115(1.667~2.666) <0.001 013.908(11.031~17.536) <0.001 2.553(1.937~3.365) <0.001
N2 08.283(6.792~10.101) <0.001 2.646(2.082~3.363) <0.001 019.466(15.651~24.211) <0.001 3.155(2.396~4.154) <0.001
M分期  
M0 1.00(参考值) 1.00(参考值)
M1 10.610(9.003~12.504) <0.001 3.593(2.911~4.435) <0.001 23.352(19.391~28.121) <0.001 4.496(3.511~5.758) <0.001
肿瘤大小(mm)  
<40 1.00(参考值) 1.00(参考值)
40~77 1.985(1.665~2.366) <0.001 1.172(1.054~1.304) 0.003 1.495(1.353~1.650) <0.001 1.231(1.017~1.490) 0.033
>77 6.290(5.300~7.465) <0.001 1.701(1.401~2.067) <0.001 2.624(2.340~2.942) <0.001 2.134(1.631~2.793) <0.001
手术方式  
未手术 1.00(参考值) 1.00(参考值)
部分切除 0.150(0.118~0.191) <0.001 0.229(0.178~0.294) <0.001 0.091(0.062~0.133) <0.001 0.159(0.106~0.239) <0.001
单纯切除 0.348(0.268~0.451) <0.001 0.490(0.373~0.644) <0.001 0.301(0.200~0.453) <0.001 0.418(0.272~0.645) <0.001
根治切除 0.403(0.320~0.506) <0.001 0.464(0.364~0.592) <0.001 0.478(0.338~0.676) <0.001 0.431(0.297~0.625) <0.001
其它方式 0.269(0.194~0.373) <0.001 0.391(0.280~0.547) <0.001 0.161(0.089~0.291) <0.001 0.336(0.183~0.618) <0.001
图2 基于多因素Cox回归分析结果的pRCC总生存率（OS）列线图模型
图3 基于多因素Cox回归分析结果的pRCC肿瘤特异性生存率（CSS）列线图模型
图4 pRCC患者计算列线图模型总分后风险分层图
图5 pRCC患者风险分层的OS及CSS生存曲线
图7 列线图模型预测pRCC患者预后的ROC曲线
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