Prognostic Factors and Nomogram for Malignant Brainstem Ependymoma: A Population‐Based Retrospective Surveillance, Epidemiology, and End Results Database Analysis
ABSTRACT Purpose This study aimed to identify prognostic factors and develop a nomogram for survival in patients with brainstem ependymoma. Methods Data of 652 patients diagnosed with brainstem ependymoma extracted from the Surveillance, Epidemiology, and End Results (SEER) registry from 2000 to 202...
Saved in:
Main Authors: | , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2025-01-01
|
Series: | Cancer Medicine |
Subjects: | |
Online Access: | https://doi.org/10.1002/cam4.70564 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
_version_ | 1832589776050454528 |
---|---|
author | Xiaoyu Ji Siyuan Yang Dejing Cheng Wenbo Zhao Xuebo Sun Fang Su |
author_facet | Xiaoyu Ji Siyuan Yang Dejing Cheng Wenbo Zhao Xuebo Sun Fang Su |
author_sort | Xiaoyu Ji |
collection | DOAJ |
description | ABSTRACT Purpose This study aimed to identify prognostic factors and develop a nomogram for survival in patients with brainstem ependymoma. Methods Data of 652 patients diagnosed with brainstem ependymoma extracted from the Surveillance, Epidemiology, and End Results (SEER) registry from 2000 to 2020 were analyzed. Univariate and multivariable Cox regression analyses were performed to examine factors influencing overall survival (OS). Receiver operating characteristic curve (ROC) and calibration curves were used to verify the nomogram. The Kaplan–Meier method was used to analyze OS based on treatment methods stratification or different age patterns. Results Six independent prognostic factors of patients with brainstem ependymoma were identified, including age, race, marital status, radiation, gross total resection (GTR), and histology. A comprehensive nomogram model was developed utilizing these predictors identified through multivariable Cox regression analysis. Furthermore, we found that patients with GTR have improved overall survival than patient with no surgery and biopsy only or with partial resection (GTR vs. no: p = 0.0004, GTR vs. partial resection: p = 0.022). Patients with radiation have improved overall survival than patient without radiation (p = 0.00013). Patients with GTR combined radiation therapy have improved overall survival than patient without or with GTR or radiation therapy only (p < 0.0001). Different treatment methods have no significant difference in the overall survival probability of the elderly group. Conclusions Individuals who are Black and anaplastic ependymomas were negative risk factors for brainstem ependymoma associated with an increased risk of mortality. Patients aged < 50 years with GTR and radiation always had better survival. |
format | Article |
id | doaj-art-88a12ffcf12c49199bc7c90c4e207883 |
institution | Kabale University |
issn | 2045-7634 |
language | English |
publishDate | 2025-01-01 |
publisher | Wiley |
record_format | Article |
series | Cancer Medicine |
spelling | doaj-art-88a12ffcf12c49199bc7c90c4e2078832025-01-24T08:46:07ZengWileyCancer Medicine2045-76342025-01-01142n/an/a10.1002/cam4.70564Prognostic Factors and Nomogram for Malignant Brainstem Ependymoma: A Population‐Based Retrospective Surveillance, Epidemiology, and End Results Database AnalysisXiaoyu Ji0Siyuan Yang1Dejing Cheng2Wenbo Zhao3Xuebo Sun4Fang Su5Department of Neurosurgery The First Affiliated Hospital of Soochow University Suzhou ChinaDepartment of Neurosurgery The First Affiliated Hospital of Soochow University Suzhou ChinaThe Fourth Affiliated Hospital of Soochow University Suzhou ChinaDepartment of Neurosurgery Second Hospital of Shanxi Medical University Taiyuan Shanxi Province ChinaDepartment of Neurosurgery The First Affiliated Hospital of Soochow University Suzhou ChinaDepartment of Neurosurgery The First Affiliated Hospital of Soochow University Suzhou ChinaABSTRACT Purpose This study aimed to identify prognostic factors and develop a nomogram for survival in patients with brainstem ependymoma. Methods Data of 652 patients diagnosed with brainstem ependymoma extracted from the Surveillance, Epidemiology, and End Results (SEER) registry from 2000 to 2020 were analyzed. Univariate and multivariable Cox regression analyses were performed to examine factors influencing overall survival (OS). Receiver operating characteristic curve (ROC) and calibration curves were used to verify the nomogram. The Kaplan–Meier method was used to analyze OS based on treatment methods stratification or different age patterns. Results Six independent prognostic factors of patients with brainstem ependymoma were identified, including age, race, marital status, radiation, gross total resection (GTR), and histology. A comprehensive nomogram model was developed utilizing these predictors identified through multivariable Cox regression analysis. Furthermore, we found that patients with GTR have improved overall survival than patient with no surgery and biopsy only or with partial resection (GTR vs. no: p = 0.0004, GTR vs. partial resection: p = 0.022). Patients with radiation have improved overall survival than patient without radiation (p = 0.00013). Patients with GTR combined radiation therapy have improved overall survival than patient without or with GTR or radiation therapy only (p < 0.0001). Different treatment methods have no significant difference in the overall survival probability of the elderly group. Conclusions Individuals who are Black and anaplastic ependymomas were negative risk factors for brainstem ependymoma associated with an increased risk of mortality. Patients aged < 50 years with GTR and radiation always had better survival.https://doi.org/10.1002/cam4.70564brainstem ependymomamortalityprognosisSEERsurvival |
spellingShingle | Xiaoyu Ji Siyuan Yang Dejing Cheng Wenbo Zhao Xuebo Sun Fang Su Prognostic Factors and Nomogram for Malignant Brainstem Ependymoma: A Population‐Based Retrospective Surveillance, Epidemiology, and End Results Database Analysis Cancer Medicine brainstem ependymoma mortality prognosis SEER survival |
title | Prognostic Factors and Nomogram for Malignant Brainstem Ependymoma: A Population‐Based Retrospective Surveillance, Epidemiology, and End Results Database Analysis |
title_full | Prognostic Factors and Nomogram for Malignant Brainstem Ependymoma: A Population‐Based Retrospective Surveillance, Epidemiology, and End Results Database Analysis |
title_fullStr | Prognostic Factors and Nomogram for Malignant Brainstem Ependymoma: A Population‐Based Retrospective Surveillance, Epidemiology, and End Results Database Analysis |
title_full_unstemmed | Prognostic Factors and Nomogram for Malignant Brainstem Ependymoma: A Population‐Based Retrospective Surveillance, Epidemiology, and End Results Database Analysis |
title_short | Prognostic Factors and Nomogram for Malignant Brainstem Ependymoma: A Population‐Based Retrospective Surveillance, Epidemiology, and End Results Database Analysis |
title_sort | prognostic factors and nomogram for malignant brainstem ependymoma a population based retrospective surveillance epidemiology and end results database analysis |
topic | brainstem ependymoma mortality prognosis SEER survival |
url | https://doi.org/10.1002/cam4.70564 |
work_keys_str_mv | AT xiaoyuji prognosticfactorsandnomogramformalignantbrainstemependymomaapopulationbasedretrospectivesurveillanceepidemiologyandendresultsdatabaseanalysis AT siyuanyang prognosticfactorsandnomogramformalignantbrainstemependymomaapopulationbasedretrospectivesurveillanceepidemiologyandendresultsdatabaseanalysis AT dejingcheng prognosticfactorsandnomogramformalignantbrainstemependymomaapopulationbasedretrospectivesurveillanceepidemiologyandendresultsdatabaseanalysis AT wenbozhao prognosticfactorsandnomogramformalignantbrainstemependymomaapopulationbasedretrospectivesurveillanceepidemiologyandendresultsdatabaseanalysis AT xuebosun prognosticfactorsandnomogramformalignantbrainstemependymomaapopulationbasedretrospectivesurveillanceepidemiologyandendresultsdatabaseanalysis AT fangsu prognosticfactorsandnomogramformalignantbrainstemependymomaapopulationbasedretrospectivesurveillanceepidemiologyandendresultsdatabaseanalysis |