Predictive Nomogram and Risk Factors for Lymph Node Metastasis in Bladder Cancer

Frontiers in Oncology

Xin Wang¹  Maolin Hu¹  Miao Wang¹  Tongxiang Diao¹  Zijian Tian²  Lingfeng Meng²  Ming Liu²  Yaqun Zhang² 

[1] Department of Urology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China;Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China;

关键词: bladder cancer;    lymph node metastasis;    prognosis;    risk;    nomogram;   

DOI  :  10.3389/fonc.2021.690324

来源: DOAJ

【 摘 要 】

Lymph node metastasis (LNM) is an important prognostic factor for bladder cancer (BCA) and determines the treatment strategy. This study aimed to determine related clinicopathological factors of LNM and analyze the prognosis of BCA. A total of 10,653 eligible patients with BCA were randomly divided into training or verification sets using the 2004–2015 data of the Surveillance, Epidemiology, and End Results database. To identify prognostic factors for the overall survival of BCA, we utilized the Cox proportional hazard model. Independent risk factors for LNM were evaluated via logistic regression analysis. T-stage, tumor grade, patient age and tumor size were identified as independent risk factors for LNM and were used to develop the LNM nomogram. The Kaplan-Meier method and competitive risk analyses were applied to establish the influence of lymph node status on BCA prognosis. The accuracy of LNM nomogram was evaluated in the training and verification sets. The areas under the receiver operating characteristic curve (AUC) showed an effective predictive accuracy of the nomogram in both the training (AUC: 0.690) and verification (AUC: 0.704) sets. In addition, the calibration curve indicated good consistency between the prediction of deviation correction and the ideal reference line. The decision curve analysis showed that the nomogram had a high clinical application value. In conclusion, our nomogram displayed high accuracy and reliability in predicting LNM. This could assist the selection of the optimal treatment for patients.

【 授权许可】

Unknown