Radiomics is the process of converting medical images into mineable data for decision support, contrasting with traditional visual interpretation. It involves extracting quantitative features from images, combining them with patient data, and using bioinformatics tools to develop models that improve diagnostic, prognostic, and predictive accuracy. Radiomics can be applied to CT, MR, and PET images, and is particularly useful in oncology. It enables the identification of tumor heterogeneity, which is critical for assessing tumor aggressiveness and prognosis. Radiomics data can be combined with genomic data to enhance decision support, a field known as radiogenomics. Challenges include ensuring reproducibility, standardization, and data quality. Radiomics has the potential to improve precision medicine by providing detailed imaging biomarkers that can inform treatment decisions. Despite challenges, radiomics is expanding beyond research into clinical practice, with efforts to establish benchmarks for data extraction, analysis, and presentation. The field is supported by initiatives like the National Cancer Institute's Quantitative Imaging Network (QIN) and the Quantitative Imaging Biomarkers Alliance (QIBA). Radiomics can help identify prognostic and predictive information, guide biopsy decisions, and improve treatment selection. However, issues such as data attrition, overfitting, and the need for large, high-quality datasets remain. Radiomics has shown promise in distinguishing cancerous from non-cancerous tissue, predicting prognosis, and guiding treatment decisions. Despite these challenges, radiomics is a rapidly evolving field with significant potential to enhance clinical decision-making.Radiomics is the process of converting medical images into mineable data for decision support, contrasting with traditional visual interpretation. It involves extracting quantitative features from images, combining them with patient data, and using bioinformatics tools to develop models that improve diagnostic, prognostic, and predictive accuracy. Radiomics can be applied to CT, MR, and PET images, and is particularly useful in oncology. It enables the identification of tumor heterogeneity, which is critical for assessing tumor aggressiveness and prognosis. Radiomics data can be combined with genomic data to enhance decision support, a field known as radiogenomics. Challenges include ensuring reproducibility, standardization, and data quality. Radiomics has the potential to improve precision medicine by providing detailed imaging biomarkers that can inform treatment decisions. Despite challenges, radiomics is expanding beyond research into clinical practice, with efforts to establish benchmarks for data extraction, analysis, and presentation. The field is supported by initiatives like the National Cancer Institute's Quantitative Imaging Network (QIN) and the Quantitative Imaging Biomarkers Alliance (QIBA). Radiomics can help identify prognostic and predictive information, guide biopsy decisions, and improve treatment selection. However, issues such as data attrition, overfitting, and the need for large, high-quality datasets remain. Radiomics has shown promise in distinguishing cancerous from non-cancerous tissue, predicting prognosis, and guiding treatment decisions. Despite these challenges, radiomics is a rapidly evolving field with significant potential to enhance clinical decision-making.