Overview

Radiogenomics uses AI to correlate imaging features with molecular and genomic data. It aims to non invasively predict tumor biology and guide targeted therapy. Integration supports personalized oncology care.

Methodology

Models combine radiomic features and deep learning representations with genomic labels. Cross validation and external cohorts validate predictive associations. Interpretability links imaging markers to biological mechanisms.

Clinical Potential

Radiogenomic signatures may predict mutation status and therapy response. They reduce need for invasive sampling in some contexts. Clinical trials evaluate impact on treatment selection.

Limitations

Heterogeneity in imaging and genomic assays complicates generalization. Large multicenter datasets and harmonization are needed. Ethical use requires clear communication about predictive uncertainty.