Introduction
Radiomics extracts quantitative features from medical images and machine learning models combine these features with clinical data to predict outcomes and treatment response. Robust feature reproducibility and external validation are prerequisites for clinical translation. Radiomics complements molecular and clinical biomarkers in precision medicine.
Pipeline and Standards
A radiomics pipeline includes standardized acquisition segmentation feature extraction and model development with rigorous cross validation. Harmonization efforts address variability across scanners and protocols. Transparent reporting and open data sharing improve reproducibility and trust.
Clinical Applications
Radiomics has shown promise in oncology for predicting response to therapy molecular subtypes and prognosis and in other fields for tissue characterization. Integration with clinical workflows requires prospective validation and demonstration of impact on management. Regulatory acceptance depends on reproducible clinical utility.
Challenges and Future Directions
Variability in imaging protocols segmentation and feature definitions limits generalizability and requires consensus standards. Combining radiomics with deep learning and multiomic data may enhance predictive power but increases complexity. Collaborative consortia and prospective trials will accelerate clinical adoption.