Language
English
Publication Date
11-25-2025
Journal
Cancers
DOI
10.3390/cancers17233762
PMID
41374965
PMCID
PMC12691521
PubMedCentral® Posted Date
11-25-2025
PubMedCentral® Full Text Version
Post-print
Abstract
Glioblastoma is an aggressive central nervous system tumor characterized by diffuse infiltration. Despite substantial advances in oncology, survival outcomes have shown little improvement over the past three decades. Radiotherapy remains a cornerstone of treatment; however, it faces several challenges, including considerable inter-observer variability in clinical target volume delineation, dose constraints associated with adjacent organs at risk, and the persistently poor prognosis of affected patients. Recent advances in artificial intelligence, particularly deep learning, have shown promise in automating radiation therapy mapping to improve consistency, accuracy, and efficiency. This narrative review explores current auto segmentation frameworks, dose mapping, and biologically informed radiotherapy planning guided by multimodal imaging and mathematical modeling. Studies have demonstrated reproducible tumor segmentations with DSCs exceeding 0.90, reduced planning within minutes, and emerging predictive capabilities for treatment response. Radiogenomic integration has enabled imaging-based classification of critical biomarkers with high accuracy, reinforcing the potential of deep learning models in personalized radiotherapy. Despite these innovations, deployment into clinical practice remains limited, primarily due to insufficient external validation and single-institution training datasets. This review emphasizes the importance of large, annotated imaging datasets, multi-institutional collaboration, and biologically explainable modeling to successfully translate deep learning into glioblastoma radiation planning and longitudinal monitoring.
Keywords
glioblastoma, artificial intelligence, deep learning, radiotherapy planning, auto segmentation, dose prediction and mapping, machine learning, personalized radiotherapy, multimodal imaging, precision oncology
Published Open-Access
yes
Recommended Citation
Master, Reid; Rubin, Nesha; Sampson, James; et al., "Advances in Artificial Intelligence for Glioblastoma Radiotherapy Planning and Treatment" (2025). The Brown Foundation: Institute of Molecular Medicine. 113.
https://digitalcommons.library.tmc.edu/molecular_med/113