Radiomics-based model for predicting neoadjuvant therapy response in esophageal cancer: Limitations and suggestions

Core Tip

Core Tip: Accurate prediction of neoadjuvant therapy response in esophageal cancer (EC) is critical to avoid ineffective treatment. Yang et al developed a non-invasive radiomics model based on T2-weighted magnetic resonance imaging (T2WI); however, it has several key limitations. These limitations include single-center, small-sample retrospective design, exclusive reliance on T2WI sequence, inadequate stability of manual segmentation, and insufficient clinical interpretability. Corresponding optimization suggestions are proposed in this editorial. Only through multicenter validation, multidisciplinary collaboration, and multimodal integration can radiomics-based machine learning models be truly translated into clinical practice, thereby supporting personalized treatment decision-making for patients with EC.