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.
Habouzit et al. (Fri,) studied this question.