Abstract

Radiomics and machine learning (ML) have emerged as transformative technologies in oncology, offering the potential for more precise, individualized cancer management. Radiomics involves the extraction of quantitative features from medical images to uncover underlying patterns, while ML algorithms are used to analyze these features for tasks such as diagnosis, prognosis, and treatment prediction. This paper discusses the integration of radiomics with ML in the context of oncology, focusing on its applications for tumor detection, classification, staging, and monitoring treatment response. The challenges associated with these technologies, including data standardization, interpretability, and clinical integration, are also explored, alongside their future potential in precision medicine.