Advances in Artificial Intelligence for Glioblastoma Radiotherapy Planning and Treatment

Area of Science:

• Neuro-oncology
• Radiation Oncology
• Artificial Intelligence in Medicine

Background:

• Glioblastoma is an aggressive brain tumor with poor survival outcomes despite advances in oncology.
• Radiotherapy is crucial but faces challenges like inter-observer variability and dose constraints.
• Deep learning offers potential for automating radiation therapy planning, improving consistency and efficiency.

Purpose of the Study:

• To review current AI frameworks for glioblastoma radiotherapy.
• To explore auto segmentation, dose mapping, and biologically informed planning.
• To highlight the potential of AI in personalized glioblastoma treatment.

Main Methods:

• Narrative review of studies on deep learning in radiotherapy.
• Analysis of auto segmentation frameworks and dose mapping techniques.
• Exploration of radiogenomic integration and predictive modeling.

Main Results:

• Deep learning achieves reproducible tumor segmentations (DSCs > 0.90) and rapid planning.
• AI models show emerging capabilities in predicting treatment response.
• Radiogenomics integration enables accurate imaging-based biomarker classification.

Conclusions:

• Deep learning shows significant promise for improving glioblastoma radiation planning and monitoring.
• Clinical deployment is limited by the need for external validation and diverse datasets.
• Multi-institutional collaboration and explainable AI are crucial for successful translation.