MRI Radiogenomics in Precision Oncology: New Diagnosis and Treatment Method

Area of Science:

• Oncology
• Radiology
• Genomics
• Bioinformatics

Background:

• Precision medicine offers tailored cancer treatments but faces challenges due to tumor heterogeneity.
• Intratumor and intertumor heterogeneity significantly impact cancer diagnosis and treatment efficacy.
• Existing research highlights the need for advanced methods to address these complexities in clinical practice.

Purpose of the Study:

• To investigate radiogenomics methods for precision cancer medicine, with a specific focus on analyzing tumor heterogeneity.
• To comprehensively analyze and discuss tumor heterogeneity using integrated multimodal imaging and molecular data.
• To develop a radiogenomics data representation model incorporating spatial-temporal correlations.

Main Methods:

• Integrative analysis of parametric imaging (e.g., breast MRI) and bulk tumor molecular/genomic data.
• Excavation of multidimensional spatial, temporal, and semantic information from high-dimensional imaging data.
• Development of a radiogenomics data representation model based on multidimensional consistency analysis and spatial-temporal correlations.

Main Results:

• Demonstrated the potential of radiogenomics to characterize tumor heterogeneity by integrating imaging and molecular data.
• Identified key spatial, temporal, and semantic features within breast MRI data relevant to tumor characteristics.
• Established a framework for a radiogenomics data representation model capturing complex tumor correlations.

Conclusions:

• Radiogenomics offers a promising avenue for advancing precision cancer medicine by addressing tumor heterogeneity.
• The integration of imaging and genomic data, analyzed through advanced models, can enhance cancer diagnosis and treatment.
• Further development and validation of radiogenomics models are crucial for clinical implementation in personalized oncology.