Published online Feb 15, 2026. doi: 10.4251/wjgo.v18.i2.114981
Revised: November 10, 2025
Accepted: November 25, 2025
Published online: February 15, 2026
Processing time: 122 Days and 16.7 Hours
A core challenge in the diagnosis and treatment of esophageal cancer (EC) lies in accurately identifying patients who will benefit from neoadjuvant therapy (NAT). Yang et al reported a predictive model for NAT response in EC, constructed using radiomics from T2-weighted magnetic resonance imaging (MRI) and machine learning. The model achieved an area under the curve of 0.932 in the training cohort and 0.900 in the validation cohort. While encouragingly, we urge caution with limitation. First, the study’s single-center, retrospective design with an insufficient sample size limits the model’s generalizability and significantly increases the risk of overfitting. Second, the study only extracted features from the T2-weighted MRI sequence, failing to integrate data from other functional MRI sequences such as diffusion-weighted imaging and dynamic contrast-enhanced MRI. Third, the model suffers from a "black box" issue regarding its extracted features—its low interpretability hinders clinicians’ trust in and acceptance of the model. This editorial reviews the study by Yang et al, identifies its limitations, and puts forward in-depth suggestions to further optimize the model.
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.