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A nomogram for predicting overall survival in patients with muscle-invasive bladder cancer undergoing radical cystectomy: a retrospective cohort study

  • 0Department of Clinical Laboratory, Peking University First Hospital, Beijing, China.
Frontiers in Oncology +

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July 4, 2025

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July 4, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study developed a prognostic nomogram to predict overall survival for muscle-invasive bladder cancer (MIBC) patients after radical cystectomy (RC). The nomogram accurately forecasts 1-, 3-, and 5-year survival, aiding clinical decision-making.

Area Of Science

  • Urology
  • Oncology
  • Medical Statistics

Background

  • Radical cystectomy (RC) is standard for muscle-invasive bladder cancer (MIBC), but survival rates are suboptimal.
  • Only half of MIBC patients survive over 5 years post-RC, highlighting a need for better prognostic tools.

Purpose Of The Study

  • To identify factors influencing overall survival (OS) in MIBC patients undergoing RC.
  • To develop and validate a prognostic nomogram for predicting 1-, 3-, and 5-year OS after RC.

Main Methods

  • Utilized data from the SEER database and Peking University First Hospital (PKUFH).
  • Employed univariate and multivariate Cox regression analyses to identify independent prognostic factors.
  • Constructed and validated a prognostic nomogram using Area Under the Curve (AUC) and decision curve analyses.

Main Results

  • The developed nomogram demonstrated high accuracy in predicting survival (1-year AUC=0.939, 3-year AUC=0.880, 5-year AUC=0.852 in the testing set).
  • The nomogram maintained good performance on an external validation dataset (1-year AUC=0.970, 3-year AUC=0.847, 5-year AUC=0.790).
  • Decision curve analysis indicated a net benefit of the nomogram over American Joint Committee on Cancer staging alone.

Conclusions

  • A validated prognostic nomogram can assist clinicians in assessing postoperative MIBC patient prognosis.
  • Future integration of more data may enhance the model's accuracy for personalized prognostics.

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