Gliomas are primary central nervous system tumors characterized by a high recurrence rate and poor prognosis, especially in high-grade forms such as glioblastoma (GBM). Radiotherapy remains a cornerstone in glioma management, particularly following surgical resection. Recent advancements in technology—including intensity-modulated radiotherapy (IMRT), proton therapy, carbon-ion radiotherapy, intraoperative radiotherapy, and ultra-high dose rate FLASH radiotherapy—have improved treatment precision and tumor control. However, clinical challenges persist due to tumor heterogeneity, imaging limitations, and planning variability. In the era of artificial intelligence (AI), novel tools such as radiomics, deep learning, and predictive modeling are increasingly being integrated into glioma radiotherapy workflows. These AI-driven approaches have shown potential to enhance imaging interpretation, automate contouring, optimize treatment planning, and predict clinical outcomes. This review highlights the evolution of glioma radiotherapy, explores the emerging role of AI across various stages of radiotherapy, and discusses future directions for implementing personalized, adaptive, and data-driven strategies in clinical practice.
Wang et al. (Thu,) studied this question.