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A Unified Methodological Framework for Vestibular Schwannoma Research
Published on: June 20, 2017
Vestibular schwannoma clinical prognostic factors and nomogram construction using SEER database
Yuyu Wei1, Jialong Tian1, Xiaojun Pang1
1Department of Neurosurgery, Zhejiang Hospital of Integrated Traditional Chinese and Western Medicine, Hangzhou, 310000, Zhejiang, China.
Objective:
This study aimed to delineate the long-term overall survival (OS) landscape and identify factors associated with OS in a large, population-based cohort of patients diagnosed with vestibular schwannoma (VS).
Materials And Methods:
Clinical, demographic, and treatment data for VS patients from the SEER database (2000-2019) were extracted and randomly divided into training and validation cohorts. Cohort comparability was assessed using Chi-square, Fisher's exact, and Mann-Whitney U tests. Univariate and multivariate Cox proportional hazards models were used to identify factors associated with OS. Nomograms for predicting 3-, 5-, and 10-year OS were constructed. Model performance was evaluated using the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).
Results:
From the initial pool of 28,693 VS patients in the SEER database, 20,235 were included in the analysis. Among these, 14,167 patients (70.01%) were allocated to the training cohort, and 6,068 patients (29.99%) to the validation cohort. Multivariate Cox regression identified age (hazard ratio [HR] = 12.895; 95% confidence interval [CI] 8.347-19.809; p < 0.001), sex (HR = 0.761; 95% CI 0.683-0.849; p < 0.001), race (HR = 0.214; 95% CI 0.089-0.515; p < 0.001), tumor size (HR = 3.024; 95% CI 2.464-3.712; p < 0.001), primary site surgery (HR = 0.409; 95% CI 0.300-0.557; p < 0.001), and radiation therapy (HR = 0.812; 95% CI 0.710-0.929; p = 0.002) as independent prognostic factors. Nomograms based on these variables demonstrated robust predictive capability, with a C-index of 0.737 ± 0.065 for the training cohort and an AUC of 0.759, 0.766, and 0.790 for predicting 3-, 5-, and 10-year OS, respectively. In the validation cohort, the model yielded a C-index of 0.763 ± 0.053 and AUCs of 0.757, 0.765, and 0.760 for the corresponding timeframes. Other factors, such as year of diagnosis, reporting source, surgical-radiation sequence, laterality, and chemotherapy, did not exhibit significant associations with OS.
Conclusion:
This analysis of over 20,000 VS patients provides a comprehensive overview of long-term survival in this patient population. We identified age, sex, race, tumor size, and treatment modality (surgery and radiotherapy) as factors associated with OS. The developed nomograms offer a tool for visualizing the combined impact of these demographic and clinical factors on long-term survival, which may contribute to comprehensive patient assessment and counseling at the time of diagnosis.
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