Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges

Area of Science:

• Oncology
• Medical Imaging
• Bioinformatics

Background:

• Predictive modeling for radiotherapy outcomes traditionally struggles with patient variability.
• Next-generation platforms, imaging, and bioinformatics offer new solutions.

Purpose of the Study:

• To discuss technical approaches for integrating diverse data (clinical, biological, imaging, treatment) for precise prediction of tumor control and radiation-induced side effects.
• To explore the emerging fields of radiogenomics and radiomics in personalized risk profiling.

Main Methods:

• Review of classical analytical and data-driven prediction approaches.
• Focus on conventional and deep machine learning in radiomics.
• Discussion of multiomics data harmonization (panomics) and model validation techniques.

Main Results:

• Radiogenomics integrates biological and clinical data for individualized risk profiles.
• Radiomics utilizes imaging features for enhanced predictive capabilities.
• Advanced machine learning and data harmonization strategies are key.

Conclusions:

• Integrating multi-modal data through radiogenomics and radiomics holds significant promise for personalized radiotherapy.
• Addressing challenges in data harmonization and model validation is crucial for clinical translation.
• These advancements can lead to improved tumor control probabilities and reduced treatment toxicity.