A nomogram model based on renal function and other readily available indicators to predict the 1-,3-,and 5-year overall survival in postoperative renal cell carcinoma patients
10.12483/j.issn.1009-8291.2026.02.003
- VernacularTitle:一个基于肾功能及其他易用指标的列线图模型：预测肾癌患者术后1、3、5年生存率
- Author:
Hui MA
1
;
Huiyu ZHOU
2
;
Peipei WANG
3
;
Zhao HOU
3
;
Qiwei WANG
2
;
Weibing SHUANG
2
Author Information
1. Department of Healthcare-Associated Infection Management, Grand Hospital of Shuozhou, Shuozhou 036000; School of Public Health, Shanxi Medical University, Taiyuan 030001; Department of Urology, The First Hospital of Shanxi Medical University, Taiyuan 030001, China
2. Department of Urology, The First Hospital of Shanxi Medical University, Taiyuan 030001, China
3. School of Public Health, Shanxi Medical University, Taiyuan 030001; Department of Urology, The First Hospital of Shanxi Medical University, Taiyuan 030001, China
- Publication Type:Journal Article
- Keywords:
renal cell carcinoma;
renal function indicators;
nomogram;
serum creatinine;
cystatin C;
survival rate;
overall survival;
radical nephrectomy;
partial nephrectomy
- From:
Journal of Modern Urology
2026;31(2):114-120
- CountryChina
- Language:Chinese
-
Abstract:
Objective To identify renal function and other readily accessible indicators influencing the prognosis of renal cell carcinoma (RCC) patients after nephrectomy, and to construct a prognostic prediction model based on them. Methods The clinical and pathological data of 624 RCC patients undergoing radical or partial nephrectomy in the First Hospital of Shanxi Medical University during Jan. 2013 and Dec. 2021 were retrospectively analyzed. The independent prognostic factors affecting postoperative overall survival (OS) were identified with LASSO algorithm combined with Cox regression analysis. Based on these factors, a nomogram was constructed to predict the 1-, 3-, and 5-year survival rates. The performance of the model was evaluated using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves and the area under the curve (AUC), calibration plots, and decision curve analysis (DCA). Finally, the cutoff value of the nomogram was calculated to stratify patients into risk groups. Kaplan-Meier survival analysis with log-rank testing was performed to compare outcomes between the risk groups. Results Nine independent prognostic factors were identified, including age, smoking history, diabetes mellitus history, maximum tumor diameter, tumor subtype, N stage, Fuhrman grade, preoperative serum creatinine and cystatin C (P＜0.05). The nomogram achieved a C-index of 0.891, with time-dependent AUC of 0.920, 0.903, and 0.867 for 1-, 3-, and 5-year OS, respectively. Calibration curves showed excellent agreement with actual observations, and DCA demonstrated significant clinical net benefits across a wide threshold probability range. Using a cutoff score of 110.4, Kaplan-Meier analysis revealed statistically significant survival differences between risk groups (P＜0.001). Conclusion The proposed nomogram, incorporating renal function and other readily accessible clinical indicators, exhibits strong predictive accuracy for postoperative survival in RCC patients and may serve as a practical tool for individualized clinical decision-making.
