Radiomics is a promising and rapidly evolving field in oncology that involves extracting quantitative, high-dimensional data from medical images to improve cancer management. Radiomics has the potential to enhance early tumor characterization, prognosis, risk stratification, treatment planning, response assessment, and surveillance. However, challenges such as data standardization, model reproducibility, transparency, and clinical integration have hindered its widespread adoption in routine clinical practice. This report evaluates the translational potential of radiomics in oncology, explores common challenges and mistakes in its development, and proposes strategies to overcome them. It emphasizes the need for multidisciplinary collaboration, rigorous validation, and addressing the perspectives of patients, healthcare providers, and healthcare systems to increase the clinical use and acceptability of radiomics. Radiomics involves two main categories of features: engineered features (intensity, shape, texture) and deep learning-derived features. Despite its potential, radiomics signatures have not yet been widely adopted in clinical practice due to issues such as data quality, sample size, and the complexity of the radiomics pipeline. Challenges include improper data management, lack of standardization, and publication bias, which can lead to overfitting and unreliable results. Strategies to address these issues include using standardized checklists, ensuring data quality, and implementing robust validation methods. The radiomics workflow involves several steps, including data collection, segmentation, preprocessing, feature extraction, selection, and model building. Each step presents unique challenges, such as segmentation errors, preprocessing biases, and feature selection issues. Addressing these requires careful planning, rigorous validation, and collaboration among multidisciplinary teams. Additionally, the use of external test sets and continuous monitoring of model performance is essential for ensuring the reliability and generalizability of radiomics signatures. The clinical translation of radiomics requires not only technical advancements but also a shift in clinical practice to incorporate radiomics into routine workflows. This includes educating healthcare providers, ensuring patient privacy, and aligning radiomics with the needs and preferences of patients and healthcare systems. The report concludes that while radiomics holds great promise, significant challenges remain that must be addressed to achieve its full clinical potential.Radiomics is a promising and rapidly evolving field in oncology that involves extracting quantitative, high-dimensional data from medical images to improve cancer management. Radiomics has the potential to enhance early tumor characterization, prognosis, risk stratification, treatment planning, response assessment, and surveillance. However, challenges such as data standardization, model reproducibility, transparency, and clinical integration have hindered its widespread adoption in routine clinical practice. This report evaluates the translational potential of radiomics in oncology, explores common challenges and mistakes in its development, and proposes strategies to overcome them. It emphasizes the need for multidisciplinary collaboration, rigorous validation, and addressing the perspectives of patients, healthcare providers, and healthcare systems to increase the clinical use and acceptability of radiomics. Radiomics involves two main categories of features: engineered features (intensity, shape, texture) and deep learning-derived features. Despite its potential, radiomics signatures have not yet been widely adopted in clinical practice due to issues such as data quality, sample size, and the complexity of the radiomics pipeline. Challenges include improper data management, lack of standardization, and publication bias, which can lead to overfitting and unreliable results. Strategies to address these issues include using standardized checklists, ensuring data quality, and implementing robust validation methods. The radiomics workflow involves several steps, including data collection, segmentation, preprocessing, feature extraction, selection, and model building. Each step presents unique challenges, such as segmentation errors, preprocessing biases, and feature selection issues. Addressing these requires careful planning, rigorous validation, and collaboration among multidisciplinary teams. Additionally, the use of external test sets and continuous monitoring of model performance is essential for ensuring the reliability and generalizability of radiomics signatures. The clinical translation of radiomics requires not only technical advancements but also a shift in clinical practice to incorporate radiomics into routine workflows. This includes educating healthcare providers, ensuring patient privacy, and aligning radiomics with the needs and preferences of patients and healthcare systems. The report concludes that while radiomics holds great promise, significant challenges remain that must be addressed to achieve its full clinical potential.