Artificial Intelligence for glioblastoma research

• Clinical outcome and deep learning imaging characteristics of patients treated by radio-chemotherapy for a "molecular" glioblastoma
• Spatial epigenomic niches underlie glioblastoma cell state plasticity
• Construction and validation of a prognostic nomogram model integrating machine learning-pathomics and clinical features in IDH-wildtype glioblastoma
• Artificial Intelligence in Glioblastoma-Transforming Diagnosis and Treatment
• Radiomics and AI-Based Prediction of MGMT Methylation Status in Glioblastoma Using Multiparametric MRI: A Hybrid Feature Weighting Approach
• Enhancing Prognostic Signatures in Glioblastoma with Feature Selection and Regularised Cox Regression
• Dissecting PTPN7-driven aggressiveness in IDH-wildtype astrocytomas: multi-omics, clinical validation, and spatial transcriptomics for prognostic insights
• Identification of intrinsic imaging subtypes using clustering analysis based on dynamic contrast-enhanced magnetic resonance imaging radiomics features for gliomas: preliminary associations with gene expression profiles

Artificial intelligence (AI) is transforming glioblastoma research across multiple domains, from diagnosis to treatment and prognosis prediction.

AI plays a crucial role in medical imaging for glioblastoma detection, segmentation, and classification: - Deep learning models (CNNs, GANs) improve MRI-based GBM detection. - Radiomics extracts quantitative imaging features to differentiate GBM subtypes. - AI-powered segmentation (e.g., using U-Net, DeepLabV3 models) automates tumor boundary detection, reducing observer variability.

Notable Applications: - BraTS Challenge datasets (Brain Tumor Segmentation) train AI models on GBM/MRI data. - Federated Learning (FL) allows AI models to learn across hospitals without sharing raw patient data.

—

AI aids in analyzing large-scale multi-omics data (genomics, transcriptomics, proteomics) to identify molecular subtypes and actionable biomarkers: - Machine learning identifies IDH mutations, MGMT promoter methylation. - AI-driven drug discovery screens for potential therapeutic targets. - Integrating radiogenomics links imaging features to molecular signatures.

Notable Applications: - AI models predict survival outcomes based on imaging-genomic correlations. - Deep learning identifies non-invasive biomarkers from liquid biopsies.

—

AI optimizes glioblastoma treatment through: - Predicting radiotherapy response using imaging + molecular data. - Personalized chemotherapy regimens via AI-based drug sensitivity modeling. - Virtual clinical trials: AI simulates patient response to experimental drugs.

Notable Applications: - AI-guided precision medicine suggests individualized treatments. - Combination therapy modeling predicts the best drug pairs for GBM.

—

AI enhances survival prediction by integrating: - Imaging features (radiomics) - Histopathological data - Clinical parameters - Genomic alterations

Deep learning models provide survival predictions that outperform traditional Kaplan-Meier curves.

—

AI accelerates glioblastoma drug discovery by:

- Identifying novel drug targets using network-based AI.

- Screening FDA-approved drugs for repurposing (e.g., AI identified anti-parasitic drugs as GBM candidates).

- Predicting blood-brain barrier (BBB) permeability for better drug selection.

Notable AI platforms: - AlphaFold (Protein Structure Prediction) - IBM Watson for Drug Discovery - DeepMind’s AI in Pharmacology

—

AI improves neurosurgical precision by: - Enhancing intraoperative imaging (real-time AI-assisted neuronavigation). - Optimizing resection margins (deep learning assists in distinguishing tumor vs. healthy tissue). - Robotic-assisted neurosurgery (AI augments robotic systems like ROSA or Brainlab).

—

- AI-based decision support tools integrate imaging, pathology, and genomics to assist oncologists. - Chatbot AI assistants help in patient education and symptom monitoring.

—

## Future Directions & Challenges - Data Privacy: Federated Learning (FL) could mitigate privacy concerns. - Model Interpretability: Black-box AI models require explainability. - Standardization: More multi-center validation of AI models needed. - Clinical Translation: AI models must be clinically validated before integration into practice.

—

## Conclusion AI is revolutionizing glioblastoma research through enhanced diagnostics, precision medicine, drug discovery, and neurosurgery support. The integration of deep learning, radiomics, genomics, and clinical AI promises improved patient outcomes. However, challenges such as data privacy, standardization, and clinical adoption remain key hurdles to overcome.