Artificial intelligence in glioma imaging: challenges and advances

Area of Science:

• Neuro-oncology
• Medical Imaging
• Artificial Intelligence

Background:

• Primary brain tumors, including gliomas, present significant clinical management challenges.
• Computed tomography (CT) and magnetic resonance imaging (MRI) are crucial for diagnosis, surgical planning, and monitoring tumor progression.
• Despite advancements, the clinical utility of AI in brain tumor imaging is limited by data, training, and reliability issues.

Purpose of the Study:

• To review recent advancements in artificial intelligence (AI) techniques for brain tumor imaging.
• To address challenges in data collection, annotation, model training, and reliability of AI-generated information.
• To facilitate the development of functional AI tools for clinical glioma care.

Main Methods:

• Review of image imputation and synthesis techniques to overcome data paucity.
• Summary of various AI training strategies for improved model performance, generalization, data privacy, and sparse annotation learning.
• Examination of standardized performance evaluation and model interpretability methods.

Main Results:

• Techniques for data augmentation and collection are summarized to address data scarcity.
• Diverse training strategies are presented to enhance AI model capabilities and address privacy concerns.
• Standardized evaluation and interpretability methods are reviewed to ensure AI reliability.

Conclusions:

• Recent technical approaches show promise in overcoming AI limitations in brain tumor imaging.
• Improved data handling, training methodologies, and evaluation metrics are key to advancing AI in neuro-oncology.
• These advancements are expected to lead to fully functional AI tools for clinical management of glioma patients.