Recent advancements in radiomics and machine learning (ML) are revolutionizing the landscape of cancer diagnostics and treatment planning. Radiomics enables the extraction of high-dimensional quantitative features from standard medical imaging modalities such as CT, MRI, and PET, transforming non-invasive scans into data-rich resources for tumor characterization. When integrated with ML algorithms, these features can facilitate predictive modeling for tumor detection, classification, prognosis, and therapeutic response, allowing for a highly personalized approach to oncology. This integration empowers clinicians to go beyond traditional histopathology by enabling in vivo phenotyping of tumors, revealing spatial heterogeneity and biological signatures that are often missed by biopsies. Numerous studies have demonstrated the utility of radiomics in various cancer types, including glioblastoma, lung, and breast cancers, while ML models such as Support Vector Machines (SVM), Random Forests, and deep learning networks like Convolutional Neural Networks (CNNs) have shown promise in enhancing classification accuracy and treatment planning. Despite its potential, challenges such as data standardization, interpretability, and clinical validation remain critical barriers to widespread implementation. This review discusses the theoretical foundation of radiomics, ML methodologies, clinical applications in tumor characterization and treatment adaptation, and future directions including radiogenomics, real-time analytics, and accessible cloud-based platforms. Addressing these issues is essential to fully realize the transformative potential of radiomics and ML in precision oncology.

Keywords: Radiomics, Machine learning, Tunour