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Drawing the Line: From U-Net-Based Glioblastoma Segmentation to Machine Learning-Driven Survival Prediction.

Costin Chirica1, Bogdan-Ionuț Dobrovăț2, Sabina-Ioana Chirica1

  • 1Faculty of Medicine, Grigore T. Popa University of Medicine and Pharmacy, 16 Universității Str., 700115 Iasi, Romania.

Medical Sciences (Basel, Switzerland)
|March 27, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence and machine learning tools can predict glioblastoma (GB) patient survival. Larger tumors and necrotic patterns negatively impact outcomes, highlighting the value of imaging biomarkers for prognosis.

Keywords:
artificial intelligenceglioblastomamachine learningpredictive modellingsurvival predictiontumor segmentation

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Area of Science:

  • Neuro-oncology
  • Computational imaging
  • Artificial intelligence in medicine

Background:

  • Glioblastoma (GB) is a highly aggressive primary malignant brain tumor with poor patient prognosis.
  • Current neuro-oncology lacks advanced computational tools for accurate prognostication.

Purpose of the Study:

  • To develop advanced computational tools for neuro-oncology.
  • To integrate AI-based segmentation and multi-model ML for glioblastoma analysis.
  • To correlate imaging biomarkers with glioblastoma patient survival.

Main Methods:

  • Retrospective analysis of 79 glioblastoma patients.
  • AI algorithms for volumetric segmentation of glioblastoma.
  • Multi-model machine learning framework for survival analysis.

Main Results:

  • Larger glioblastoma tumors correlated with shorter post-treatment survival.
  • Tumor necrosis patterns affected patient survival and therapy response.
  • Volumetric, shape, and morphological imaging metrics predicted patient outcomes.
  • Neural Network and Random Forest models demonstrated strong predictive performance.

Conclusions:

  • AI-based segmentation and ML models enhance neuro-oncology computational tools.
  • Imaging biomarkers derived from quantitative volumetric analysis are crucial for glioblastoma prognosis.
  • This approach aids in understanding glioblastoma progression and patient outcomes.