Wang J, Yang Y, Pan J, Qiu Y, Shen S, Wang W. Competing-risk nomogram for predicting survival in patients with advanced (stage III/IV) gallbladder cancer: A SEER population-based study.
Jpn J Clin Oncol 2022;
52:353-361. [PMID:
35137118 DOI:
10.1093/jjco/hyab212]
[Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 12/22/2021] [Indexed: 02/05/2023] Open
Abstract
OBJECTIVE
The primary aim of this study was to assess the cumulative incidence of cause-specific mortality (CSM) and other cause-specific mortality (OCSM) for patients with advanced gallbladder cancer (GBC), and then to develop a nomogram based on competing-risk analysis to forecast CSM.
METHODS
We identified the patients with GBC with specific screening criteria and from the Surveillance Epidemiology and End Results (SEER) database. We calculated the cumulative incidence function for CSM and OCSM, and constructed a competing-risk nomogram based on the Fine and Gray's proportional subdistribution hazard regression model to forecast the probability of CSM of these patients. In addition, the concordance index and calibration plot were performed to validate the novel established model.
RESULTS
A total of 1411 patients were included in this study. The 1-, 2-, and 3-year overall cumulative mortalities were 46.2, 62.2, and 69.6% for CSM, respectively, while they were 6.2, 8.7, and 10.4% for OCSM. Additionally, the 1-, 2-, and 3-year estimates of overall survival were 47.6, 29.1, and 19.9% for above these patients, respectively. We also developed a competing-risk nomogram to estimate the CSM. The concordance index was 0.775 (95% confidence interval (CI): 0.750-0.800) in the training set and that was 0.765 (95% CI: 0.730-0.800) in the internal validation set, which suggests the robustness of the novel established model. Furthermore, the calibration curves and concordance index demonstrated that the nomogram was well-calibrated and demonstrated good discriminative ability.
CONCLUSIONS
The ample sample allowed us to develop a reliable model which demonstrated better calibration and discrimination for predicting the probability of CSM of patients with advanced GBC.
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