Deep Learning With Radiogenomics Towards Personalized Management of Gliomas.

Area of Science:

• Radiogenomics
• Artificial Intelligence in Medicine
• Neuro-oncology

Background:

• Gliomas are common brain tumors requiring advanced diagnostic and management strategies.
• Integrating noninvasive imaging with genomic data offers personalized patient care.
• Current research focuses on multi-modal radiogenomic approaches for improved outcomes.

Purpose of the Study:

• To provide a comprehensive survey of interdisciplinary radiogenomic approaches for glioma diagnosis and management.
• To highlight the application of deep learning in extracting features from medical images.
• To explore the integration of imaging and genomic data for personalized medicine.

Main Methods:

• Utilizing deep learning and transfer learning for automated feature extraction from radiomic data.
• Analyzing gene expression values from tumor tissues alongside imaging data.
• Investigating associations between radiomic and genomic features to identify imaging surrogates.
• Leveraging magnetic resonance imaging (MRI) data for model training and validation.

Main Results:

• Deep learning effectively extracts relevant features from tumor radioimages.
• Multi-modal approaches enhance the understanding of glioma characteristics.
• Radiogenomic analysis aids in identifying imaging surrogates for genomic features.
• Studies demonstrate potential in survival prediction and decision-making.

Conclusions:

• Multi-modal radiogenomic approaches are crucial for advancing glioma diagnosis and personalized management.
• Deep learning plays a pivotal role in analyzing complex radiomic and genomic data.
• Further research integrating imaging and genomic information promises improved patient outcomes in neuro-oncology.