A Review of AI and Radiomics: Translating Cancer Images into Quantitative Data

Radiomics and AI transform cancer imaging into quantitative phenotypic data that correlates with tumor biology. This review surveys classical and deep learning approaches and highlights the need for robust, reproducible methods before clinical adoption.

• Radiomics high-throughput extraction of imaging features converts scans into structured data for cancer phenotyping.
• Both handcrafted (classical) and deep learning-based radiomic approaches are covered, with applications in diagnosis, prognosis, and management.
• Reproducibility and robustness challenges must be overcome to move radiomics from research to routine practice.

Automated summary

RadPigeon summaries are original and for information only. They are not clinical advice.