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Background <p>The use of traditional Cox regression models to identify risk factors for distant metastasis (DM) after RNU (Radical nephroureterectomy) in UTUC (upper urinary tract urothelial carcinoma) patients may introduce bias. This study aims to utilize a large UTUC dataset from our center and apply the Fine-Gray model to determine predictive factors for DM. Additionally, we manage to construct a prediction model that can accurately estimate the likelihood of DM following RNU.</p> Methods <p>A retrospective analysis was conducted on the clinical and pathological data of 2,546 patients with UTUC from Peking University First Hospital. Univariate and multivariate Fine-Gray competing risk models were employed to identify independent predictive factors for the occurrence of DM. Subsequently, a clinical nomogram was developed based on these factors. The predictive performance of the nomogram was rigorously evaluated using the C-index, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Patients were stratified into two distinct risk categories according to their nomogram scores, the prognostic outcomes and potential treatment strategies for different risk groups were then compared and analyzed.</p> Results <p>Our study identified six independent predictors of DM: hydronephrosis, tumor dimensions, tumor architecture, surgical margin, pathological T stage, and N stage. A nomogram-based clinical model constructed using these predictors demonstrated excellent predictive performance, with C-indices of 0.781, 0.825, and 0.830 in the training set, validation set 1, and validation set 2, respectively. Additionally, the net benefit of this nomogram-based scoring system was superior to that of the AJCC model. High-risk populations identified using this scoring system may derive significant benefit from chemotherapy, potentially altering their prognostic outcomes.</p> Conclusions <p>Our study identified independent risk factors for the development of DM following surgery in patients with UTUC. The clinical nomogram developed based on these factors demonstrates satisfactory predictive performance and holds significant clinical utility. This tool can assist clinicians in adjusting follow-up strategies and developing personalized treatment options for individual patients.</p>

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Competing risk analysis for predicting distant metastasis in patients with upper urinary tract urothelial carcinoma following radical nephroureterectomy: based on a large dataset from a national medical center

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月21日

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  • Kun Peng,
  • Bao Guan,
  • Han Hao,
  • Jianye Zhang,
  • Guoli Wang,
  • Wei Zuo,
  • Qi Tang,
  • Yicong Du,
  • Zihao Tao,
  • Chunru Xu,
  • Zheng Zhang,
  • Yi Yang,
  • Liqun Zhou,
  • Xuesong Li,
  • Xiaoying Li

摘要

Background

The use of traditional Cox regression models to identify risk factors for distant metastasis (DM) after RNU (Radical nephroureterectomy) in UTUC (upper urinary tract urothelial carcinoma) patients may introduce bias. This study aims to utilize a large UTUC dataset from our center and apply the Fine-Gray model to determine predictive factors for DM. Additionally, we manage to construct a prediction model that can accurately estimate the likelihood of DM following RNU.

Methods

A retrospective analysis was conducted on the clinical and pathological data of 2,546 patients with UTUC from Peking University First Hospital. Univariate and multivariate Fine-Gray competing risk models were employed to identify independent predictive factors for the occurrence of DM. Subsequently, a clinical nomogram was developed based on these factors. The predictive performance of the nomogram was rigorously evaluated using the C-index, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Patients were stratified into two distinct risk categories according to their nomogram scores, the prognostic outcomes and potential treatment strategies for different risk groups were then compared and analyzed.

Results

Our study identified six independent predictors of DM: hydronephrosis, tumor dimensions, tumor architecture, surgical margin, pathological T stage, and N stage. A nomogram-based clinical model constructed using these predictors demonstrated excellent predictive performance, with C-indices of 0.781, 0.825, and 0.830 in the training set, validation set 1, and validation set 2, respectively. Additionally, the net benefit of this nomogram-based scoring system was superior to that of the AJCC model. High-risk populations identified using this scoring system may derive significant benefit from chemotherapy, potentially altering their prognostic outcomes.

Conclusions

Our study identified independent risk factors for the development of DM following surgery in patients with UTUC. The clinical nomogram developed based on these factors demonstrates satisfactory predictive performance and holds significant clinical utility. This tool can assist clinicians in adjusting follow-up strategies and developing personalized treatment options for individual patients.