International Journal For Multidisciplinary Research

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Integration of Artificial Intelligence in Predicting Radiotherapy Outcomes for Glioblastoma Multiforme (GBM)

Author(s) Dr. Shikhar Bindal, Dr. Shahil Aftab Hussain, Dr. Ahmed Sarhang Belal, Dr. Parasa Leela Lakshmi Sai Srinivas, Dr. Christian Inya Oko
Country India
Abstract Glioblastoma multiforme (GBM) is an aggressive and lethal brain tumorwith limited treatment options and a poor prognosis. Radiotherapy is acornerstone of GBM management, yet predicting treatment responseremains a challenge due to tumor heterogeneity. Artificial intelligence(AI), particularly machine learning (ML) and deep learning (DL), hasemerged as a promising tool for enhancing predictive accuracy andpersonalizing treatment strategies. This systematic review evaluates thecurrent advancements in AI-driven models for predicting radiotherapyoutcomes in GBM patients. A comprehensive search of PubMed,Scopus, and Web of Science identified 35 relevant studies employing
various AI methodologies, including ML, DL, and hybrid approaches. Theresults indicate that convolutional neural networks (CNNs) and hybrid AImodels incorporating radiomics and genetic biomarkers achieved thehighest predictive performance, with accuracy rates ranging from 75%to 92% and area under the curve (AUC) values up to 0.91. Despite theseadvancements, challenges such as data heterogeneity, small samplesizes, and model interpretability remain significant barriers to clinicalimplementation. Future research should focus on large-scalemulticenter collaborations, the integration of multi-omics data, and thedevelopment of explainable AI (XAI) models to enhance transparencyand clinical applicability.
Keywords Glioblastoma multiforme, Artificial intelligence, Machine learning, Deep learning, Radiotherapy outcomes, Predictive modeling, Radiomics, Explainable AI, Personalized treatment.
Field Biology
Published In Volume 7, Issue 3, May-June 2025
Published On 2025-06-19
DOI https://doi.org/10.36948/ijfmr.2025.v07i03.41679
Short DOI https://doi.org/g9qxc9
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