Development of a predictive model for recurrence-free survival in pTa low-grade bladder cancer

Jorge Daza, Ralph Grauer, Sophie Chen, Etienne Lavallèe, Shirin Razdan, Linda Dey, Gunnar Steineck, Lotta Renström-Koskela, Qiang Li, Ahmed A. Hussein, Reza Mehrazin, Nikhil Waingankar, Khurshid Guru, Peter Wiklund, John P. Sfakianos

Research output: Contribution to journalArticlepeer-review


Background: Data on Ta low-grade (LG) non-muscle invasive bladder cancer (NMIBC) have shown that follow-up cystoscopies are normal in 82% and 67% of patients with single and multiple tumors, respectively. Objective: To develop a predictive model associated with recurrence-free survival (RFS) at 6, 12, 18 and 24 months in TaLG cases that consider the patients’ risk aversion. Materials and methods: Data from a prospectively maintained database of 202 newly diagnosed TaLG NMIBC patients treated at Scandinavian institutions were used for the analysis. To identify risk groups associated with recurrence, we performed a classification tree analysis. Association between risk groups and RFS was evaluated by Kaplan Meier analysis. A Cox proportional hazard model selected significant risk factors associated with RFS using the variables defining the risk groups. The reported C index for the Cox model was 0.7. The model was internally validated and calibrated using 1000 bootstrapped samples. A nomogram to estimate RFS at 6, 12, 18, and 24 months was generated. The performance of our model was compared to EUA/AUA stratification using a decision curve analysis (DCA). Results: The tree classification found that tumor number, tumor size and age were the most relevant variables associated with recurrence. The patients with the worst RFS were those with multifocal or single, ≥ 4cm tumors. All the relevant variables identified by the classification tree were significantly associated with RFS in the Cox proportional hazard model. DCA analysis showed that our model outperformed EUA/AUA stratification and the treat all/none approaches. Conclusion: We developed a predictive model to identify TaLG patients that benefit from less frequent follow-up cystoscopy schedule based on the estimated RFS and personal recurrence risk aversion.

Original languageEnglish
Pages (from-to)256.e9-256.e15
JournalUrologic Oncology: Seminars and Original Investigations
Issue number5
StatePublished - May 2023


  • Bladder cancer
  • Low grade
  • Non-muscle invasive
  • Outcomes
  • Predictive model


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