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Background <p>To establish an effective dynamic nomogram and a novel risk classification system to predict overall survival (OS) for Renal Cell Carcinoma with venous tumor thrombus (RCC-VTT).</p> Methods <p>318 patients&#xa0;were enrolled and randomly divided into a training set and a validation set in a 7:3 ratio. LASSO regression analysis and multivariate Cox regression analysis were employed to identify significant prognostic factors. Based on these factors, a nomogram model was developed and evaluated using the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCA). Survival differences&#xa0;were assessed using Kaplan–Meier curves and the log-rank test.</p> Results <p>Eight survival predictors were identified: Mayo Clinic Stage, Histology, N Stage, M Stage, Renal Sinus Invasion, Sarcomatoid Feature, Hemoglobin, and Estimated Glomerular Filtration Rate. The C-indexes for the training and validation sets were 0.77 (95% CI: 0.72–0.82) and 0.75 (95% CI: 0.68–0.82), respectively. The AUCs for the training and validation sets were 0.869 (95% CI: 0.805–0.933) and 0.854 (95% CI: 0.770–0.937) for the 5-yr predictions, respectively. DCA further confirmed the clinical utility of the&#xa0;model. Additionally, the nomogram-based classification system stratified patients into distinct risk subgroups for OS&#xa0;(<i>P</i> &lt; 0.0001).</p> Conclusions <p>We developed a dynamic&#xa0;nomogram and novel risk classification system for&#xa0;RCC-VTT. This tool has the potential to personalize&#xa0;treatment strategies.</p>

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A dynamic nomogram and risk classification for predicting prognosis in renal cell carcinoma with venous tumor thrombus

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月18日

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  • ZeZhen Zhou,
  • Liyuan Ge,
  • Fan Zhang,
  • ShaoHui Deng,
  • HongXian Zhang,
  • GuoLiang Wang,
  • Min Lu,
  • ShuDong Zhang

摘要

Background

To establish an effective dynamic nomogram and a novel risk classification system to predict overall survival (OS) for Renal Cell Carcinoma with venous tumor thrombus (RCC-VTT).

Methods

318 patients were enrolled and randomly divided into a training set and a validation set in a 7:3 ratio. LASSO regression analysis and multivariate Cox regression analysis were employed to identify significant prognostic factors. Based on these factors, a nomogram model was developed and evaluated using the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCA). Survival differences were assessed using Kaplan–Meier curves and the log-rank test.

Results

Eight survival predictors were identified: Mayo Clinic Stage, Histology, N Stage, M Stage, Renal Sinus Invasion, Sarcomatoid Feature, Hemoglobin, and Estimated Glomerular Filtration Rate. The C-indexes for the training and validation sets were 0.77 (95% CI: 0.72–0.82) and 0.75 (95% CI: 0.68–0.82), respectively. The AUCs for the training and validation sets were 0.869 (95% CI: 0.805–0.933) and 0.854 (95% CI: 0.770–0.937) for the 5-yr predictions, respectively. DCA further confirmed the clinical utility of the model. Additionally, the nomogram-based classification system stratified patients into distinct risk subgroups for OS (P < 0.0001).

Conclusions

We developed a dynamic nomogram and novel risk classification system for RCC-VTT. This tool has the potential to personalize treatment strategies.