Development and external validation of a model to predict recurrence in patients with non-muscle invasive bladder cancer

Background Most patients initially diagnosed with non-muscle invasive bladder cancer (NMIBC) still have frequent recurrence after urethral bladder tumor electrodesiccation supplemented with intravesical instillation therapy, and their risk of recurrence is difficult to predict. Risk prediction models used to predict postoperative recurrence in patients with NMIBC have limitations, such as a limited number of included cases and a lack of validation. Therefore, there is an urgent need to develop new models to compensate for the shortcomings and potentially provide evidence for predicting postoperative recurrence in NMIBC patients. Methods Clinicopathologic characteristics and follow-up data were retrospectively collected from 556 patients with NMIBC who underwent transurethral resection of bladder tumors by electrocautery (TURBT) from January 2014 to December 2023 at the Affiliated Hospital of Zunyi Medical University and 167 patients with NMIBC who underwent the same procedure from January 2018 to April 2024 at the Third Affiliated Hospital of Zunyi Medical University. Independent risk factors affecting the recurrence of NMIBC were screened using the least absolute shrinkage and selection operator (Lasso) and Cox regression analysis. Cox risk regression models and randomized survival forest (RSF) models were developed. The optimal model was selected by comparing the area under the curve (AUC) of the working characteristics of the subjects in both and presented as a column-line graph. Results The study included data from 566 patients obtained from the affiliated hospital of Zunyi Medical University and 167 patients obtained from the third affiliated hospital of Zunyi Medical University. Tumor number, urine leukocytes, urine occult blood, platelets, and red blood cell distribution width were confirmed as independent risk factors predicting RFS by Lasso-Cox regression analysis. The Cox proportional risk regression model and RSF model were constructed based on Lasso, which showed good predictive efficacy in both training and validation sets, especially the traditional Cox proportional risk regression model. In addition, the discrimination, consistency, and clinical utility of the column-line graph were assessed using C-index, area under the curve (AUC), calibration curve, and decision curve analysis (DCA). Patients at high risk of recurrence can be identified early based on risk stratification. Conclusion Internal and external validation has demonstrated that the model is highly discriminative and stable and can be used to assess the risk of early recurrence in NMIBC patients and to guide clinical decision-making.