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Cancer Survival Analysis01:21

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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: Jan 7, 2026

Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections
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Machine Learning-Enhanced Prognostic Modeling in Elderly Glioblastoma Isocitrate Dehydrogenase-Wildtype: A

Noa Ben Dor1, Filippo Friso2, Gal Ziv3

  • 1Department of Neurosurgery, IRCCS Institute of Neurological Sciences of Bolognat, Bologna, Italy; Department of Biomorphology and Neuromotor Sciences (DIBINEM), Alma Mater Studiorum University of Bolognat, Bologna, Italy.

World Neurosurgery
|December 24, 2025
PubMed
Summary

For elderly glioblastoma patients, gross total resection, MGMT methylation, and chemoradiotherapy significantly improve survival. Machine learning models highlight BMI and cognitive decline as key preoperative predictors.

Keywords:
ElderlyExtent of resectionGlioblastoma prognosisMachine learningPersonalized precision oncologyPreoperative risk stratification

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Area of Science:

  • Neuro-oncology
  • Geriatric Medicine
  • Computational Biology

Background:

  • Elderly glioblastoma (GBM IDHwt) patients face challenges due to heterogeneity and underrepresentation in trials.
  • Current prognostication is inadequate, leading to subjective treatment decisions.

Purpose of the Study:

  • Identify survival determinants in elderly GBM IDHwt patients.
  • Evaluate machine learning (ML) models for prognostic utility using clinical and pre-treatment data.

Main Methods:

  • Analysis of 155 elderly GBM IDHwt patients (≥70 years) undergoing neurosurgery.
  • Multivariate regression and Histogram Gradient Boosting Regression (HGBR) ML models applied to clinical, radiological, surgical, and molecular data.
  • Two ML models developed: one with full data, one with pre-treatment data only.

Main Results:

  • Median overall survival (OS) was 11.3 months (resection) vs. 3.7 months (biopsy).
  • Independent OS predictors: gross total resection (GTR), MGMT promoter methylation, non-acute symptom onset, radiotherapy + temozolomide (RT + TMZ).
  • ML models confirmed RT + TMZ and GTR; highlighted BMI and cognitive decline as preoperative predictors.

Conclusions:

  • GTR, MGMT methylation, and RT + TMZ are confirmed prognostic factors.
  • Baseline KPS and age lacked independent prognostic value; BMI and cognitive decline are potential preoperative predictors.
  • A multidimensional, data-driven approach for preoperative risk stratification may enable individualized glioblastoma treatment strategies.