Glioblastoma overall survival [Neurosurgery Wiki]

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====== Glioblastoma overall survival ======

{{rss>https://pubmed.ncbi.nlm.nih.gov/rss/search/1vWAR2vnuIcrenqgcnvlWNqR36nXEKHhsqndpRfCiouqvrqQ98/?limit=15&utm_campaign=pubmed-2&fc=20250127023917}}
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see also [[Glioblastoma prognosis]].
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[[Overall survival]] (OS) in [[glioblastoma]] patients varies depending on factors such as age, performance status, extent of surgical resection, and molecular characteristics of the tumor. 

===== Median Overall Survival =====

Standard of Care: For patients treated with maximal safe surgical resection, followed by radiation therapy with concurrent and adjuvant temozolomide (Stupp protocol), the median OS is approximately 14–18 months.
Without Treatment: Median survival drops to approximately 3–6 months.

===== Long-Term Survival =====

2-Year Survival: Around 25–30% of patients.

5-Year Survival: Rare, seen in about 5% of patients, often linked to favorable molecular features like IDH mutations. Not really [[glioblastoma]].

===== Molecular and Genetic Factors =====

IDH Mutation:

GBM with IDH mutation (IDH-mutant glioblastoma) has a better prognosis with longer OS compared to IDH-wildtype GBM.

MGMT Promoter Methylation:

MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation predicts better response to temozolomide and is associated with longer OS.

TERT Promoter Mutations:

Common in GBM but generally associated with poorer prognosis unless paired with other favorable markers.

1p/19q Co-deletion:

Rare in GBM and more characteristic of oligodendrogliomas, with a better prognosis in these cases.

Emerging Treatments and Survival Impact

Tumor Treating Fields (TTF): Can extend median survival by a few months when used in conjunction with standard therapy.

Immunotherapy: Limited success so far, but ongoing trials may change outcomes.

Targeted Therapy: Exploring pathways like EGFR, VEGF, and others has shown mixed results.

Clinical Trials: Participation in trials offers access to novel treatments that might improve outcomes.

While progress has been made, GBM remains one of the most challenging cancers to treat, with a strong focus on extending quality of life alongside survival.








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In [[glioblastoma]], [[progression-free survival]] ([[PFS]]) and [[overall survival]] (OS) are strongly correlated, indicating that PFS may be an appropriate surrogate for OS. Compared with OS, PFS offers earlier assessment and higher statistical power at the time of analysis 
((Han K, Ren M, Wick W, Abrey L, Das A, Jin J, Reardon DA. Progression-free
survival as a surrogate endpoint for overall survival in glioblastoma: a
literature-based meta-analysis from 91 trials. Neuro Oncol. 2013 Dec 12. [Epub
ahead of print] PubMed PMID: 24335699.)).

Increasing the [[extent of resection]] (EOR) of [[Glioblastoma]] is associated with prolonged [[survival]]
((Sanai N, Polley MY, McDermott MW, Parsa AT, Berger MS (2011)
An extent of resection threshold for newly diagnosed glioblastomas. J Neurosurg 115:3–8. https://doi.org/10.3171/2011.2.
jns1099810.3171/2011.7.jns10238))

Also, adjuvant [[radiochemotherapy]] showed higher survival rates in patients with [[complete
resection]] (EOR ≥ 90%), compared with [[partial resection]] (EOR < 90%)
(( Stummer W, van den Bent MJ, Westphal M (2011) Cytoreductive
surgery of glioblastoma as the key to successful adjuvant therapies:
new arguments in an old discussion. Acta Neurochir 153:1211–
1218. https://doi.org/10.1007/s00701-011-1001-x)).
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[[Glioblastoma IDH Mutant]] is associated with better outcome and increased overall survival
((Ohgaki H, Kleihues P. The definition of primary and secondary glioblastoma. Clin Cancer Res. 2013 Feb 15;19(4):764-72. doi: 10.1158/1078-0432.CCR-12-3002. Epub 2012 Dec 3. PMID: 23209033.)).
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Overall estimates of [[survival]] among patients with [[glioblastoma]] have at least doubled since [[2005]] to 18% at 2 years and 11% at 3 years. This may reflect treatment response to modern therapeutic approaches. However, longer-term survival remains poor and there appears to be a lack of improvement in 5-year survival
((Poon MTC, Sudlow CLM, Figueroa JD, Brennan PM. Longer-term (≥ 2 years) survival in patients with glioblastoma in population-based studies pre- and post-2005: a systematic review and meta-analysis. Sci Rep. 2020 Jul 15;10(1):11622. doi: 10.1038/s41598-020-68011-4. PMID: 32669604; PMCID: PMC7363854.)).
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[[Magnetic resonance perfusion imaging]] parameter obtained on 3-Tesla and the [[Ki-67]] labeling index predict the [[overall survival]] of glioblastoma
((Fudaba H, Momii Y, Matsuta H, Onishi K, Kawasaki Y, Sugita K, Shimomura T, Fujiki M. Perfusion parameter obtained on 3-Tesla magnetic resonance imaging and the Ki-67 labeling index predict the overall survival of glioblastoma. World Neurosurg. 2021 Feb 7:S1878-8750(21)00183-2. doi: 10.1016/j.wneu.2021.02.002. Epub ahead of print. PMID: 33567368.)).
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MR image derived texture features, tumor shape and volumetric features, and patient age were obtained for 163 patients. OS group prediction was performed for both 2-class (short and long) and 3-class (short, medium and long) survival groups. Support vector machine classification based recursive feature elimination method was used to perform feature selection. The performance of the classification model was assessed using 5-fold cross-validation. The 2-class and 3-class OS group prediction accuracy obtained were 98.7% and 88.95% respectively. The shape features used in this work have been evaluated for OS prediction of Glioblastoma patients for the first time. The feature selection and prediction scheme implemented in this study yielded high accuracy for both 2-class and 3-class OS group predictions. This study was performed using routinely acquired MR images for Glioblastoma patients, thus making the translation of this work into a clinical setup convenient
((Sanghani P, Ang BT, King NKK, Ren H. Overall survival prediction in glioblastoma multiforme patients from volumetric, shape and texture features using machine learning. Surg Oncol. 2018 Dec;27(4):709-714. doi: 10.1016/j.suronc.2018.09.002. Epub 2018 Sep 10. PMID: 30449497.)).

===== Retrospective, multi-institutional observational studies =====
A [[retrospective]] study included patients diagnosed with [[glioblastoma]] from the multicenter [[BraTioUS]] database. A single 2D iUS slice, showing the largest tumor diameter, was selected for each patient. Radiomic features were extracted and subjected to feature selection, and clinical data were collected. Using a fivefold cross-validation strategy, [[Cox]] proportional hazards models were built using radiomic features alone, clinical data alone, and their combination. Model performance was assessed via the concordance index (C-index).

A total of 114 patients met the [[inclusion criteria]], with a mean age of 56.88 years, a median OS of 382 days, and a median preoperative tumor volume of 32.69 cm3. Complete tumor resection was achieved in 51.8% of the patients. In the testing cohort, the combined model achieved a mean C-index of 0.87 (95% CI: 0.76-0.98), outperforming the radiomic model (C-index: 0.72, 95% CI: 0.57-0.86) and the clinical model (C-index: 0.73, 95% CI: 0.60-0.87).

[[Intraoperative ultrasound]] relies on acoustic properties for tissue characterization, capturing unique features of glioblastomas. This study demonstrated that radiomic features derived from this imaging modality have the potential to support the development of [[survival]] models
((Cepeda S, Esteban-Sinovas O, Singh V, Moiyadi A, Zemmoura I, Del Bene M, Barbotti A, DiMeco F, West TR, Nahed BV, Giammalva GR, Arrese I, Sarabia R. Prognostic Modeling of Overall Survival in Glioblastoma Using Radiomic Features Derived from Intraoperative Ultrasound: A Multi-Institutional Study. Cancers (Basel). 2025 Jan 16;17(2):280. doi: 10.3390/cancers17020280. PMID: 39858063.)).
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This study makes a significant contribution to the emerging field of intraoperative [[radiomics]] by demonstrating the prognostic potential of iUS features in [[glioblastoma]] patients. However, its [[limitation]]s, particularly the small [[sample size]], retrospective design, and lack of [[external validation]], necessitate further research to confirm the findings and establish their clinical utility. By addressing these gaps, future studies can ensure that such models become valuable tools for optimizing treatment strategies in [[glioblastoma management]].

===== References =====