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Related Experiment Video

Updated: Jan 15, 2026

Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures
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Radiogenomics for Glioblastoma Survival Prediction: Integrating Radiomics, Clinical, and Genomic Features Using

Sebastian Buzdugan1, Moona Mazher2, Domenec Puig3

  • 1Department of Computer Engineering and Mathematics, Universitat Rovira I Virgili, Tarragona, Spain. buzdugan_sebastian@yahoo.com.

Journal of Imaging Informatics in Medicine
|October 8, 2025
PubMed
Summary
This summary is machine-generated.

This study enhances glioblastoma (GBM) survival prediction by integrating imaging, clinical, and molecular data using machine learning. An optimized dense neural network (Dense NN) model shows superior performance in forecasting patient outcomes.

Keywords:
GlioblastomaMGMT promoter methylationMRIMachine learningRadiogenomicsRadiomics featuresSurvival analysis

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Area of Science:

  • Neuro-oncology
  • Artificial Intelligence in Medicine
  • Radiomics

Background:

  • Glioblastoma (GBM) is a highly aggressive brain tumor with variable prognosis.
  • Accurate survival prediction is crucial for personalized treatment strategies.
  • Current prognostic models often lack integration of diverse data types.

Purpose of the Study:

  • To develop and validate an advanced machine learning model for improved glioblastoma survival prediction.
  • To integrate imaging phenotypes, clinical characteristics, and molecular markers for a comprehensive prognostic assessment.
  • To compare the performance of a dense neural network (Dense NN) against traditional machine learning algorithms.

Main Methods:

  • Analysis of two multi-institutional cohorts (UPENN-GBM and UCSF-PDGM) comprising isocitrate dehydrogenase (IDH) wild-type GBM cases.
  • Extraction of radiomic features from MRI data using specialized tools.
  • Application of machine learning algorithms including Random Forest, XGBoost, LightGBM, and an optimized Dense NN.
  • Hyperparameter tuning and model optimization for the Dense NN architecture.

Main Results:

  • The optimized Dense NN model achieved superior predictive performance compared to tree-based algorithms.
  • Concordance indices (CI) of 0.86 (UPENN-GBM) and 0.83 (UCSF-PDGM) were obtained with the Dense NN.
  • MGMT promoter methylation was associated with significantly longer median survival (504 days vs. 329 days).

Conclusions:

  • An integrative, non-invasive approach combining AI, imaging, clinical, and molecular data enhances glioblastoma survival prediction.
  • The optimized Dense NN model offers a robust framework for personalized therapeutic strategies in GBM.
  • This methodology advances precision medicine by improving prognostic accuracy for GBM patients.