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Verhaak, R.G.W., Hoadley, K.A., Purdom, E., Wang, V., Qi, Y., Wilkerson, M.D., et al. (2010) Integrated Genomic Analysis Identifies Clinically Relevant Subtypes of Glioblastoma Characterized by Abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell, 17, 98-110.
https://doi.org/10.1016/j.ccr.2009.12.020

has been cited by the following article:

• TITLE: Advancing Glioblastoma Prognosis: A Review of Machine Learning, Radiomics, and Multi-Omics Integration for Survival Prediction and Subtyping

  AUTHORS: Muna Awel, Rushit Dave, Mansi Bhavsar

  KEYWORDS: GBM, Survival Prediction, Machine Learning (ML), Deep Learning (DL), Radiomics, Multi-Omics, Feature Engineering Clustering

  JOURNAL NAME: Journal of Computer and Communications, Vol.13 No.5, May 23, 2025

  ABSTRACT: Glioblastoma (GBM) is known for its poor prognosis and aggressive nature, driving the need for advanced models that provide survival prediction to improve patient prognosis. This literature review synthesizes 20 studies employing machine learning (ML), deep learning (DL), radiomics, and clustering to enhance GBM prognosis using clinical, imaging, and molecular data. The review is grouped into five thematic groups based on their methodology and data type. Studies within these groups explore the predictive accuracy of models, evaluation metrices, and data types of implementations. The research also highlights the limitations which include small datasets, sparse clinical variables, computational complexity, and hindering scalability and generalizability. This review provides a clear understanding of the various studies that are aimed at enhancing patient prognosis in glioblastoma through advanced predictive and subtyping methodologies.