Abstract

BackgroundA survival benefit was observed in metastatic bladder cancer patients who underwent primary tumor resection, but it was still confusing which patients are suitable for the surgery. For this purpose, we developed a model to screen stage M1 patients who would benefit from primary tumor resection.MethodsPatients with metastatic bladder cancer were screened from the Surveillance, Epidemiology, and End Results database (2004–2016) and then were divided into surgery (partial or complete cystectomy) group and non-surgery group. To balance the characteristics between them, a 1:1 propensity score matching analysis was applied. A hypothesis was proposed that the received primary tumor resection group has a more optimistic prognosis than the other group. The multivariable Cox model was used to explore the independent factors of survival time in two groups (beneficial and non-beneficial groups). Logistic regression was used to build a nomogram based on the significant predictive factors. Finally, a variety of methods are used to evaluate our model.ResultsA total of 7,965 patients with metastatic bladder cancer were included. And 3,314 patients met filtering standards, of which 545 (16.4%) received partial or complete cystectomy. Plots of the Kaplan–Meier and subgroup analyses confirmed our hypothesis. After propensity score matching analysis, a survival benefit was still observed that the surgery group has a longer median overall survival time (11.0 vs. 6.0 months, p < 0.001). Among the surgery cohort, 303 (65.8%) patients lived longer than 6 months (beneficial group). Differentiated characteristics included age, gender, TNM stage, histologic type, differentiation grade, and therapy, which were integrated as predictors to build a nomogram. The nomogram showed good discrimination in both training and validation cohorts (area under the receiver operating characteristic curve (AUC): 0.806 and 0.742, respectively), and the calibration curves demonstrated good consistency. Decision curve analysis showed that the nomogram was clinically useful. Compared with TNM staging, our model shows a better predictive value in identifying optimal patients for primary tumor resection.ConclusionsA practical predictive model was created and verified, which might be used to identify the optimal candidates for the partial or complete cystectomy group of the primary tumor among metastatic bladder cancer.

Highlights

  • Bladder cancer is the 2nd most commonly diagnosed urologic neoplasm worldwide, with approximately 573,000 new cases and new 213,000 deaths in 2020 [1]

  • Patients diagnosed with bladder cancer were selected during a study period of 2004 to 2016 from the SEER database by the SEER*Stat software (8.3.9) according to the primary site

  • There were 545 (16.4%) Metastatic bladder cancer (mBC) patients who received surgery on the primary tumor (PTR group), and 290 (69.7%) patients benefited from surgery

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Summary

Introduction

Bladder cancer is the 2nd most commonly diagnosed urologic neoplasm worldwide, with approximately 573,000 new cases and new 213,000 deaths in 2020 [1]. A survival benefit for patients with metastatic tumors after surgery on the primary site has been observed, such as metastatic esophageal cancer and metastatic non-small cell lung cancer [8,9,10]. A survival benefit was observed in metastatic bladder cancer patients who underwent primary tumor resection, but it was still confusing which patients are suitable for the surgery. For this purpose, we developed a model to screen stage M1 patients who would benefit from primary tumor resection

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