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Updated: May 5, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and validation of a nomogram for predicting overall survival in patients with primary central nervous
Dunchen Yao1, Baokui Ye2, Hongli Zhang3
1Department of Oncology, The Second People's Hospital of Guiyang, Guiyang, China.
Background:
Primary central nervous system (CNS) germ cell tumors (GCTs) are common neoplasms in the CNS of pediatric and adolescent patients. This study aimed to identify prognostic factors associated with CNS GCTs and establish an effective nomogram for predicting overall survival (OS) in patients with CNS GCTs.
Methods:
The development cohort including 1166 CNS GCTs patients was selected from Surveillance, Epidemiology, and End Results (SEER) program between 2000 and 2021. An additional 165 CNS GCTs patients treated at the Sun Yat-sen University Cancer Center (SYSUCC) between 1997 and 2019 were included as validation cohort.
Results:
The nomogram incorporated the variables screened by multivariate Cox regression analysis, which included age, sex, histopathology, dissemination, tumor size, radiotherapy, and chemotherapy. The model demonstrated good discriminative performance, with C-index of 0.773 (95% CI, 0.734 - 0.812) and 0.712 (95% CI, 0.599- 0.825) in the development and validation cohorts, respectively. Calibration curves and area under the time-dependent receiver operating characteristic curve (time-dependent AUC) verified the superiority of our nomogram for clinical usefulness. Decision curve analysis (DCA) further illustrated the potential clinical value of the nomogram for treatment decision-making. Additionally, we established a comprehensive risk grouping system that effectively categorized patients into distinct prognostic groups based on their predicted outcomes.
Conclusion:
A precise prognostic nomogram was developed for patients with CNS GCTs, utilizing seven independent prognostic factors. It demonstrated satisfactory performance and clinical usability, aiding clinicians in accurately estimating prognosis and guiding the treatment and long-term management of patients with CNS GCTs.
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