Artificial Intelligence in Glioblastoma-Transforming Diagnosis and Treatment.

Abstract

Glioblastoma (GBM) is the most aggressive and common primary brain malignancy in adults, characterized by poor prognosis and treatment resistance. Despite advancements in treatment options, the median survival is roughly 15 months, underlining the need for novel and effective treatments. Artificial intelligence (AI) has emerged as a transformative technology in healthcare, offering outstanding capabilities in data analysis, pattern recognition, and helping in decision-making. This review explores the current and potential role of AI in GBM care, focusing on its applications in diagnosis, treatment planning, prognostication, and drug discovery. AI-based algorithms have demonstrated promising potential in enhancing diagnostics through imaging analysis, radiomics, and tumor segmentation. These technologies could enable non-invasive molecular profiling and early detection of GBM. In treatment planning, AI could improve approaches by optimizing surgical resection, radiotherapy regimen, and chemotherapy protocols. Furthermore, machine learning models can integrate multimodal data to develop personalized treatments. They can also enhance prognostication by predicting survival, recurrence, and treatment responses, helping clinicians to make more informed decisions. AI is also revolutionizing pharmacotherapy by identifying novel molecular targets and optimizing combination therapies. Despite notable progress, challenges persist. Limited data quality and quantity, algorithm interpretability, integration problems, and ethical considerations, remain significant challenges to clinical implementation. This review emphasizes the need for continued research and interdisciplinary collaboration to overcome many barriers and realize the transformative potential of AI in GBM care.