AI-Powered Histology for Molecular Profiling in Brain Tumors: Toward Smart Diagnostics from Tissue

Area of Science:

• Neuro-oncology
• Computational pathology
• Molecular diagnostics

Background:

• The World Health Organization (WHO) classification of central nervous system (CNS) tumors integrates molecular features for improved diagnosis and precision medicine.
• Unequal access to molecular testing hinders the universal application of integrated diagnostics for brain tumors.
• Artificial intelligence (AI) offers a potential solution by predicting molecular alterations directly from histological data.

Purpose of the Study:

• To review the current progress of AI-assisted molecular profiling prediction for brain tumors using histological data.
• To highlight the potential of AI in overcoming diagnostic accessibility challenges in neuro-oncology.
• To explore the application of AI in both permanent and intraoperative tissue sections.

Main Methods:

• Deep learning models applied to whole-slide images (WSIs) of permanent sections for glioma biomarker prediction (IDH mutation, 1p/19q co-deletion), molecular subtyping, and outcome prediction.
• AI applied to intraoperative cryosections for real-time glioma grading and molecular prediction.
• Exploration of novel modalities like stimulated Raman histology and domain-adaptive image translation for label-free tissue analysis.
• AI-driven morphology-based molecular classification for spinal cord ependymomas and intraoperative discrimination of gliomas from primary CNS lymphomas.

Main Results:

• AI models achieve neuropathologist-level accuracy in predicting key molecular biomarkers and subtypes in gliomas from WSIs.
• AI enables real-time analysis of intraoperative cryosections, facilitating immediate clinical decisions.
• AI-powered histology shows promise in classifying other brain tumors and differentiating tumor types intraoperatively.
• AI integration with histology offers a pathway for rapid, accurate, and globally accessible neuro-oncology diagnostics.

Conclusions:

• AI-driven prediction of molecular alterations from histology is a rapidly advancing field with significant potential in neuro-oncology.
• AI-assisted diagnostics can democratize access to integrated tumor classification, improving patient care worldwide.
• The synergy between histology and computational methods is poised to revolutionize brain tumor diagnosis and management.