Development and Validation of a Novel Recurrence Risk Stratification for Initial Non-Muscle Invasive Bladder Cancer in the Han Chinese Population

Background: Some classification models for determining the risk of recurrence after transurethral resection of bladder tumor (TURBT) in patients with non-muscle invasive bladder cancer (NMIBC) had some shortcomings in clinical applications. This study aimed to investigate whether the European Organization for Research and Treatment of Cancer (EORTC) risk stratification was useful to predict the recurrence of NMIBC in the Han Chinese population. In addition, we developed and validated a novel risk stratification method for recurrence prediction of NMIBC. Methods: Excluding cases who do not meet the inclusion criteria, 606 patients with NMIBC from the First Affiliated Hospital of Zhengzhou University were included in the testing and validation groups. The recurrence-free survival (RFS) curve according to the EORTC risk classifications was calculated by the Kaplan-Meier and the log-rank test methods. Receiver operating characteristic (ROC) curve analysis was used to estimate the diagnosis value for recurrence. We built a logistic regression model for recurrence in NMIBC patients combining the independent recurrence prognostic factors. One external validation group including 166 patients with NMIBC from the Zhongnan Hospital of Wuhan University was also used to assess the logistic regression model. Results: There was no significant difference in RFS rates between the groups grouped according to EORTC. We constructed a novel risk model to predict recurrence by classifying patients into two groups using ten independent prognostic factors [bladder cancer-specific nuclear matrix protein 4 (BLCA-4), bladder tumour antigen (BTA), nuclear matrix protein 22 (NMP22), carcinoembryonic antigen (CEA), body mass index, smoking, family history of bladder cancer, occupational exposure to aromatic amine chemicals, number of tumours, bladder instillation of chemotherapeutic agents] to predict tumour recurrence based on logistic regression analyses (testing group). According to the novel recurrence risk classification, there was a significant difference in 5-year RFS rates between the low-risk group and the high-risk group (Validation group and the external validation group). Conclusions: Our novel classification model can be a useful tool to predict recurrence risk in the Han Chinese population with NMIBC.