Editorial: Radiomics and artificial intelligence in oncology imaging

illustrate both the potential and the current limitations of radiomics-and AI-driven methodologies across a wide range of tumor types and imaging modalities.One of the strongest messages emerging from this collection is the particular value of AI in diagnostically equivocal or "gray-zone" clinical situations. In such settings, several original contributions illustrate how AI-driven imaging models may support lesion characterization and disease stratification. More broadly, this paradigm may support more dynamic disease assessment through repeated evaluation of intra-patient inter-and intra-lesional heterogeneity over time, and may help inform prognostic assessment beyond what is typically achievable with conventional tissue sampling.Treatment response prediction was another major focus of this research topic. In a systematic review and meta-analysis, Rodriguez et al. compared AI-based models with radiologist evaluation and reported a modest but statistically significant advantage of AI for predicting treatment response in lung cancer, particularly in CT and PET/CT imaging.Radiomics was further applied to treatment-related toxicity prediction. In nasopharyngeal carcinoma, Hong et al. developed a combined machine-learning model integrating clinical, radiomic, and dosiomic features, which demonstrated higher predictive accuracy than singledomain models for acute radiation-induced dermatitis. Such approaches may enable pretherapeutic risk stratification and support personalized preventive strategies.Despite these encouraging results, several authors appropriately caution against This Research Topic illustrates a field transitioning from methodological exploration toward clinically meaningful integration. Radiomics and AI appear to show their greatest value in improving decision-making in complex and uncertain oncologic scenarios. With continued efforts toward standardization, validation, and interpretability, these approaches may play an increasingly important role in the future of precision oncology.