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Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

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Related Experiment Video

Updated: Jul 7, 2026

Optimization of High Grade Glioma Cell Culture from Surgical Specimens for Use in Clinically Relevant Animal Models and 3D Immunochemistry
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Survival prediction in glioblastoma: 10-year follow-up from the Dutch Neurosurgery Quality Registry.

Jeroen T J M van Dijck1,2, Hilko Ardon3, Rutger K Balvers4

  • 1Department of Neurosurgery, Haaglanden Medical Centre, The Hague, The Netherlands.

Journal of Neuro-Oncology
|May 23, 2025
PubMed
Summary

Glioblastoma survival remains poor despite advances. Surgical resection and adjuvant therapy improve outcomes, but age and molecular markers significantly influence prognosis. New models aid patient-centered care.

Keywords:
GlioblastomaPatient outcomePrognostic modelSurvival

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Area of Science:

  • Neuro-oncology
  • Clinical Epidemiology
  • Surgical Oncology

Background:

  • Glioblastoma is a highly aggressive primary brain tumor with poor outcomes.
  • Standard treatments offer limited efficacy, necessitating improved prognostic tools.

Purpose of the Study:

  • To evaluate survival and prognostic factors in glioblastoma patients.
  • To develop pragmatic prognostic models for individualized patient care.
  • To analyze treatment variations and outcomes in a large Dutch cohort.

Main Methods:

  • Prospective cohort analysis of 7621 glioblastoma patients (2012-2022).
  • Univariate analysis, Kaplan-Meier curves, and Cox regression for prognostic modeling.
  • Funnel plots assessed center performance and surgical volume impact.

Main Results:

  • Median survival was 10.4 months; 2-year survival was 17.8%.
  • Factors influencing survival included age, ASA classification, Karnofsky Performance Status, extent of resection, complications, MGMT methylation, and adjuvant therapy.
  • Prognostic models achieved C-indices of 0.704 (6-month) and 0.721 (2-year).

Conclusions:

  • Surgical resection and adjuvant therapy improve glioblastoma survival but prognosis remains poor.
  • Age, premorbid condition, treatment, and molecular markers are key survival determinants.
  • Developed prognostic models show potential for clinical decision-making after validation.