Radiomics is a quantitative method in medical imaging that uses advanced mathematical analysis to extract textural information from images, enhancing clinical decision-making. It involves analyzing signal intensity distribution and pixel relationships using AI techniques. Despite its potential, radiomics faces challenges due to technical factors affecting feature extraction. This review outlines a typical radiomics workflow, provides a "how-to" guide, and discusses current limitations, suggesting improvements and summarizing relevant literature. Key steps in radiomics include image segmentation, processing, feature extraction, and selection. Segmentation can be manual, semi-automated, or fully automated using deep learning. Image processing involves homogenizing images and discretizing intensities. Feature extraction quantifies texture, shape, and intensity, while feature selection reduces dimensionality to improve model accuracy. Radiomics studies face challenges such as lack of standardization, insufficient reporting, and limited open-source data. These issues affect reproducibility and clinical utility. Studies show that radiomic features are sensitive to technical factors like voxel size, image discretization, and acquisition settings. To improve robustness, guidelines and quality checklists are recommended, along with the use of digital phantoms and open-source data. Recent research emphasizes the need for standardized protocols, including IBSI guidelines, to ensure reproducibility and clinical relevance. Open-source data and tools like 3D Slicer and pyRadiomics aid in improving radiomics reproducibility. However, challenges remain in translating radiomics into clinical practice due to variability in imaging protocols and the need for robust, generalizable features. The review highlights the importance of quality control, standardization, and transparency in radiomics research. Future studies should focus on harmonizing datasets, improving feature selection, and ensuring clinical validation. Radiomics should be viewed as an additional tool rather than a standalone diagnostic method, with ongoing efforts to integrate it into clinical workflows. Overall, radiomics holds promise for enhancing diagnostic and prognostic capabilities but requires further research to address current limitations and ensure its clinical utility.Radiomics is a quantitative method in medical imaging that uses advanced mathematical analysis to extract textural information from images, enhancing clinical decision-making. It involves analyzing signal intensity distribution and pixel relationships using AI techniques. Despite its potential, radiomics faces challenges due to technical factors affecting feature extraction. This review outlines a typical radiomics workflow, provides a "how-to" guide, and discusses current limitations, suggesting improvements and summarizing relevant literature. Key steps in radiomics include image segmentation, processing, feature extraction, and selection. Segmentation can be manual, semi-automated, or fully automated using deep learning. Image processing involves homogenizing images and discretizing intensities. Feature extraction quantifies texture, shape, and intensity, while feature selection reduces dimensionality to improve model accuracy. Radiomics studies face challenges such as lack of standardization, insufficient reporting, and limited open-source data. These issues affect reproducibility and clinical utility. Studies show that radiomic features are sensitive to technical factors like voxel size, image discretization, and acquisition settings. To improve robustness, guidelines and quality checklists are recommended, along with the use of digital phantoms and open-source data. Recent research emphasizes the need for standardized protocols, including IBSI guidelines, to ensure reproducibility and clinical relevance. Open-source data and tools like 3D Slicer and pyRadiomics aid in improving radiomics reproducibility. However, challenges remain in translating radiomics into clinical practice due to variability in imaging protocols and the need for robust, generalizable features. The review highlights the importance of quality control, standardization, and transparency in radiomics research. Future studies should focus on harmonizing datasets, improving feature selection, and ensuring clinical validation. Radiomics should be viewed as an additional tool rather than a standalone diagnostic method, with ongoing efforts to integrate it into clinical workflows. Overall, radiomics holds promise for enhancing diagnostic and prognostic capabilities but requires further research to address current limitations and ensure its clinical utility.