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September 10, 2025Discover OncologyOpen Access

Machine learning and deep learning in glioblastoma: a systematic review and meta-analysis of diagnosis, prognosis, and treatment

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Authors

HTHadja Fatima TbahritiABAli BoukadoumMBMeriem Benbernou

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Overview

Systematic review examines the impact of machine learning and deep learning on diagnosis and prognosis in glioblastoma, suggesting improved accuracy in clinical tasks.

Key Points

  • Machine learning and deep learning techniques demonstrate strong performance in various clinical tasks for glioblastoma.
  • The pooled C-index for overall survival prognosis was found to be 0.78, indicating reliable predictive ability.
  • Tumor segmentation models achieved a high average Dice Similarity Coefficient of 0.91, showcasing advanced segmentation accuracy.
  • Despite promising results, the applicability of AI techniques in glioblastoma is limited by data variation and external validation issues.

Cite This Study

Tbahriti et al. (2025) studied this question.

synapsesocial.com/papers/68c1bb7854b1d3bfb60edd7bhttps://doi.org/10.1007/s12672-025-03303-7
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Drawing the Line: From U-Net-Based Glioblastoma Segmentation to Machine Learning-Driven Survival Prediction2026 · 1 citations
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  3. 3Machine Learning Models for Predicting Pseudoprogression in Glioblastoma: A Systematic Review and Diagnostic Meta-Analysis2026
  4. 4Advancing Brain Tumor Diagnosis Using Deep Learning: A Systematic and Critical Review on Methodological Approaches to Glioma Segmentation and Classification Through Multiparametric MRI2026
  5. 5Automated MRI-Based Brain Tumor Segmentation and SurvivalPrediction Using Deep Learning and Machine Learning Techniques2026