Glioblastoma overall survival

Glioblastoma overall survival

In glioblastomaprogression-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 1).

Increasing the extent of resection (EOR) of GBM is associated with prolonged survival 2)

Also, adjuvant radiochemotherapy showed higher survival rates in patients with complete resection (EOR ≥ 90%), compared with partial resection (EOR < 90%) 3).


Glioblastoma IDH Mutant is associated with better outcome and increased overall survival 4).


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 5).


Magnetic resonance perfusion imaging parameter obtained on 3-Tesla and the Ki-67 labeling index predict the overall survival of glioblastoma 6).


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 GBM 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 GBM patients, thus making the translation of this work into a clinical setup convenient 7).


1)

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.
2)

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
3)

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
4)

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.
5)

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.
6)

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.
7)

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.

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