A Prognostic Nomogram Based on Log Odds of Positive Lymph Nodes to Predict Overall Survival for Non-Metastatic Bladder Cancer Patients after Radical Cystectomy

Author:

Yang Jingtian,Huang Huasheng,Li Wenshuang,Ran Shengming,Hu Jintao,Zhang Yishan,Li Wenjie,Chen Changhao,He Wang

Abstract

(1) Purpose: The purpose of this study was to evaluate the prognostic capacity of the pathological N status (pN), lymph node ratio (LNR), and the log odds of positive lymph nodes (LODDS), and to build a prognostic nomogram to predict overall survival (OS) for bladder cancer patients treated by radical cystectomy. (2) Methods: The clinical and pathological characteristics of 10,938 patients with bladder cancer were identified from the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2017. The predictive capacity was assessed by univariate and multivariate Cox regression analyses, the area under the receiver operating characteristic curve (AUC), and C-index. Calibration curves, decision curve analysis (DCA), and risk-grouping were utilized to evaluate the predictive accuracy and discriminative ability of the nomogram. (3) Results: LODDS was an independent risk factor for bladder cancer (all p < 0.001) and demonstrated the highest values of C-index and AUC. The values of AUCs in the training cohort were 0.747, 0.743, and 0.735 for predicting 1-, 3-, and 5-year OS, respectively. Calibration curves and DCA curves suggested the excellent clinical application value of our nomogram. (4) Conclusions: LODDS is a better predictive indicator for bladder cancer patients compared to pN and LNR. The LODDS-incorporated nomogram has excellent accuracy and promising clinical application value for non-metastatic bladder cancer after radical cystectomy.

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Key Areas Research and Development Program of Guangdong

Science and Technology Planning Project of Guangdong Province

Guangdong Clinical Research Center for Urological Diseases

Yixian Youth project of Sun Yat-sen Memorial Hospital

Young Teacher Training Funding of Sun Yat-sen University

Science and Technology Program of Guangzhou, China

Natural Science Foundation of Guangdong Province, China

Publisher

MDPI AG

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