Artificial Intelligence-Driven Multi-Omics Approaches in Glioblastoma.

Area of Science:

• Neuro-oncology
• Computational Biology
• Bioinformatics

Background:

• Glioblastoma (GBM) is an aggressive primary brain tumor with significant heterogeneity, leading to varied clinical outcomes.
• High-throughput omics technologies have advanced GBM understanding and molecular classification for precision medicine.
• Analyzing vast and complex omics data requires substantial computational resources.

Purpose of the Study:

• To explore the potential of integrating multi-omics, imaging radiomics, and clinical data with AI for GBM research.
• To enhance the understanding of GBM molecular profiling, prognosis, and treatment strategies.
• To improve the clinical management of glioblastoma through advanced computational approaches.

Main Methods:

• Review of artificial intelligence (AI) applications, including machine learning (ML) and deep learning (DL), in analyzing GBM data.
• Integration of multi-omics data (genomics, transcriptomics, epigenomics) with imaging radiomics and clinical information.
• Exploration of AI-driven computational approaches for GBM data processing, analysis, and interpretation.

Main Results:

• AI offers a unique opportunity to infer biological insights from complex GBM datasets.
• Integration of multi-omics, radiomics, and clinical data with AI can uncover critical aspects of GBM.
• AI facilitates a deeper understanding of GBM heterogeneity and its implications for patient outcomes.

Conclusions:

• AI, particularly ML and DL, holds significant promise for advancing glioblastoma research and clinical management.
• Integrating diverse data types with AI is crucial for overcoming the challenges posed by GBM complexity.
• AI-driven insights can lead to improved molecular profiling, prognosis prediction, and personalized treatment strategies for GBM patients.