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Prognostic Factors of Gliosarcoma in the Real World: A Retrospective Cohort Study.

Authors :
Yu, Ziye
Zhou, Zhirui
Xu, Ming
Song, Kun
Shen, Jingjing
Zhu, Wenhao
Wei, Liqun
Xu, Hongzhi
Source :
Computational & Mathematical Methods in Medicine. 1/30/2023, p1-14. 14p.
Publication Year :
2023

Abstract

Purpose. Gliosarcoma is a histopathological variant of glioblastoma, which is characterized by a biphasic growth pattern consisting of glial and sarcoma components. Owing to its scarcity, data regarding the impact of available treatments on the clinical outcomes of gliosarcoma are inadequate. The purpose of this retrospective cohort study was to analyze the prognostic factors of gliosarcoma. Methods. By screening the clinical database of neurosurgical cases at a single center, patients with gliosarcoma diagnosed histologically from 2013 to 2021 were identified. Clinical, pathological, and molecular data were gathered founded on medical records and follow-up interviews. Prognostic factors were derived using the Cox proportional hazards model with backward stepwise regression analysis. Results. Forty-five GSM patients were included. Median overall survival was 25.6 months (95% CI 8.0–43.1), and median relapse-free survival was 15.2 months (95% CI 9.7–20.8). In multivariable analysis, total resection (p = 0.023 , HR = 0.192 , 95% CI 0.046–0.797) indicated an improved prognosis. And low expression of Ki-67 (p = 0.059 , HR = 2.803 , 95% CI 0.963–8.162) would be likely to show statistical significance. However, there might be no statistically significant survival benefit from radiotherapy with concurrent temozolomide (n = 33 , 73.3%, log-rank p = 0.99) or adjuvant temozolomide (n = 32 , 71.1%, log-rank p = 0.74). Conclusion. This single-center retrospective study with a limited cohort size has demonstrated the treatment of gross total resection and low expression of Ki-67 which are beneficial for patients with GSM, while radiotherapy or temozolomide is not. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1748670X
Database :
Academic Search Index
Journal :
Computational & Mathematical Methods in Medicine
Publication Type :
Academic Journal
Accession number :
161583302
Full Text :
https://doi.org/10.1155/2023/1553408