AUTHOR=Hu Jintao , Zheng Zhenming , Zheng Junjiong , Xie Weibin , Su Huabin , Yang Jingtian , Xu Zixin , Shen Zefeng , Yu Hao , Fan Xinxiang , Kong Jianqiu , Han Jinli 

TITLE=A Model for Identifying Optimal Patients for Primary Tumor Resection in Patients With Metastatic Bladder Cancer

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.809664

DOI=10.3389/fonc.2021.809664

ISSN=2234-943X

ABSTRACT=Background: A survival benefit was observed in metastatic bladder cancer patients who underwent primary tumor resection, but it was still confused 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.
Methods: Patients 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 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 survival time of 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.
Results: 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 Kaplan-Meier and subgroup analysis 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 characters 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 both in training and validation cohorts (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.
Conclusions: A 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.