AI in Neuro-Oncology: Advances in Glioma Diagnosis, Molecular Profiling, and Precision Treatment
This NPJ Precision Oncology review surveys AI applications across the full spectrum of brain tumor management, from imaging-based detection and molecular subtype inference to outcome prediction and adaptive treatment planning. The authors highlight that AI models can discern molecular features from imaging alone, potentially reducing reliance on invasive tissue sampling. Promising future directions include multimodal data integration, generative AI, large medical language models, and addressing racial and gender disparities in model performance.
The original study
Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment.
- Authors
- Khalighi S, Reddy K, Midya A, Pandav KB, Madabhushi A, Abedalthagafi M
- Journal
- NPJ precision oncology
- Type
- Journal Article, Review
- PMID
- 38553633
Original abstract
This review delves into the most recent advancements in applying artificial intelligence (AI) within neuro-oncology, specifically emphasizing work on gliomas, a class of brain tumors that represent a significant global health issue. AI has brought transformative innovations to brain tumor management, utilizing imaging, histopathological, and genomic tools for efficient detection, categorization, outcome prediction, and treatment planning. Assessing its influence across all facets of malignant brain tumor management- diagnosis, prognosis, and therapy- AI models outperform human evaluations in terms of accuracy and specificity. Their ability to discern molecular aspects from imaging may reduce reliance on invasive diagnostics and may accelerate the time to molecular diagnoses. The review covers AI techniques, from classical machine learning to deep learning, highlighting current applications and challenges. Promising directions for future research include multimodal data integration, generative AI, large medical language models, precise tumor delineation and characterization, and addressing racial and gender disparities. Adaptive personalized treatment strategies are also emphasized for optimizing clinical outcomes. Ethical, legal, and social implications are discussed, advocating for transparency and fairness in AI integration for neuro-oncology and providing a holistic understanding of its transformative impact on patient care.