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Updated: Sep 12, 2025
Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections
Published on: July 31, 2017
Machine learning and deep learning in glioblastoma: a systematic review and meta-analysis of diagnosis, prognosis,
Hadja Fatima Tbahriti1,2, Ali Boukadoum3,4, Meriem Benbernou3
1Higher School of Biological Sciences of Oran, Oran, Algeria. tbfatima@gmail.com.
Introduction:
Glioblastoma (GBM) is the most malignant primary brain cancer, associated with a median overall survival of 15 months. Traditional diagnosis and prognosis heavily rely on clinical examination and histological investigation, both of which are subjective and time-consuming. advances in machine learning (ML) and deep learning (DL) have largely accelerated the research of GBMs by enhancing tumour segmentation, molecular characterization and survival prediction.
Methodology:
We refer to the PRISMA guidelines to report this systematic review and meta-analysis. A total of 44 studies published from 2021 to 2025 were analyzed. We thoroughly searched the following sources: PubMed, Scopus and Web of Science. Review-specific inclusion criteria included studies reporting on diagnostic, prognostic, or response-prediction tasks in GBM that used ML/DL models and reports on quantitative performance metrics. The independent random-effects model estimated the performance of each clinical task, and subgroup analysis determined the variables influencing model accuracy.
Results:
The performance of the machine and deep learning models was strong across different clinical tasks. For overall survival prognosis, the pooled C-index was 0.78 (95%CI 0.74-0.82, I2 = 68.5%). The tumor segmentation models had a high average Dice Similarity Coefficient value (0.91, 95% CI 0.87-0.94, I2 = 45.2%). Molecular tests were highly accurate for the prediction of IDH1 mutation (pooled accuracy = 90.5%, 95% CI 88.1% to 92.8%) and MGMT methylation status (pooled accuracy = 97.8%, 95% CI 96.4% to 99.1%). Transformer models excelled over CNN in segmentation, and radionics-based ML could improve non-invasive molecular assessment.
Conclusion:
Although AI techniques have demonstrated encouraging results in GBM studies for various clinical tasks, substantial challenges still preclude efficient clinical applicability. These developments can potentially improve medical practice with improved diagnosis, personalized treatment and fewer invasive procedures. Nevertheless, variation in data, weak external validation, and missing prospective clinical studies warrant careful interpretation of these results.
