Overview

Radiomics converts images into quantitative features for analysis and modeling. AI automates feature extraction and selection for predictive tasks. These features support precision medicine and research.

Feature Stability

Reproducibility of radiomic features depends on acquisition and reconstruction parameters. Harmonization and standardization improve comparability across centers. Phantom studies help assess feature stability.

Clinical Applications

Radiomic signatures predict treatment response prognosis and molecular profiles in oncology. Integration with clinical and genomic data enhances predictive power. Prospective validation is required for clinical use.

Data Governance

Large curated datasets with standardized annotations enable robust model development. Data sharing frameworks and privacy preserving methods support multicenter research. Transparent reporting of methods ensures reproducibility.