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Related Experiment Video

Updated: Sep 10, 2025

Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections
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Estimating overall survival of glioblastoma patients using clinical variables, tumor size, and location.

Alexandros Ferles1,2, Paulina Majewska3,4, Ragnhild Holden Helland5,6

  • 1Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam, The Netherlands.

Neuro-Oncology Advances
|August 22, 2025
PubMed
Summary

Clinical factors and tumor characteristics significantly impact glioblastoma prognosis. A Deep Survival model effectively predicts patient survival, aiding treatment decisions.

Keywords:
deep neural networksglioblastomamagnetic resonance imagingsurvival analysis

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Area of Science:

  • Neuro-oncology
  • Medical imaging analysis
  • Machine learning in healthcare

Background:

  • Accurate glioblastoma prognosis is vital for effective treatment planning and improved patient outcomes.
  • This study explores the prognostic value of clinical variables, tumor size, and location in glioblastoma.
  • Identifying reliable prognostic factors can enhance disease management strategies.

Purpose of the Study:

  • To evaluate the prognostic significance of clinical variables, tumor size, and location for glioblastoma patient survival.
  • To compare the performance of different survival regression models in predicting overall survival.
  • To determine the optimal stage for prognostic assessment in the patient treatment pathway.

Main Methods:

  • A retrospective multicenter study included 1318 glioblastoma patients.
  • Pre- and post-operative MRI data were analyzed for tumor size, location, and residual volume.
  • Survival prediction models (CoxPH, Random Survival Forests, DeepSurv) were applied and evaluated using C-index and Brier Scores.

Main Results:

  • Multivariable Cox analysis confirmed clinical variables and tumor size as significant survival predictors.
  • The DeepSurv model demonstrated superior performance across all timepoints, with C-index scores from 61.71% to 70.29%.
  • Integrated Brier Scores for DeepSurv ranged from 7.63% to 8.57%.

Conclusions:

  • Clinical variables, tumor size, and location are valuable prognostic indicators for glioblastoma.
  • A Deep Survival model integrating all variables offers the best predictive accuracy, particularly at the chemoradiotherapy planning stage.