Reply to “Enhancing Postoperative Decision‐Making in Esophageal Cancer: Clinical Perspectives and Future Research Needs”

We sincerely thank Wu et al. for their thoughtful and constructive comments on our recent publication entitled “Tumor Regression Grade as a Predictor of Adjuvant Therapy Benefits in Esophageal Squamous Cell Carcinoma Patients After Neoadjuvant Therapy [1].” Their insights highlight several important methodological and clinical considerations that merit further clarification. First, regarding the categorization of postoperative adjuvant therapy, we acknowledge the concern about combining chemotherapy and chemo-immunotherapy into a single analytical group. In our cohort of 416 patients, 253 (60.8%) received adjuvant treatment, of whom the majority (87.0%) underwent chemotherapy alone, while only a small proportion received combined chemo-immunotherapy. Given this distribution, a unified categorization was adopted to preserve statistical power and avoid unstable estimates in underrepresented subgroups. We recognize that this approach introduces therapeutic heterogeneity. As noted in our original discussion, the limited sample size of patients receiving chemo-immunotherapy precluded meaningful subgroup analysis. With the increasing adoption of immunotherapy in the perioperative setting, future studies with larger cohorts will be essential to delineate whether the observed survival benefit is consistent across treatment modalities or predominantly driven by specific regimens. Second, we agree that retrospective analyses are inherently susceptible to immortal time bias and selection bias. To mitigate these risks, our study was restricted to patients who underwent curative (R0) resection and were followed within a standardized surveillance framework. Multivariable Cox regression models were applied to adjust for key clinical covariates, including age and performance status. Importantly, in the tumor regression grade (TRG) 0–1 subgroup, adjuvant therapy remained independently associated with improved survival after adjustment (HR 0.32, p = 0.006). Nevertheless, we acknowledge that residual confounding cannot be fully eliminated, particularly regarding unmeasured factors influencing treatment allocation, such as frailty or postoperative recovery status [2]. Prospective validation, ideally through dedicated clinical trials or rigorously designed target trial emulation studies, will be crucial to establish causality. Third, we concur with the concern regarding inter-observer variability in TRG assessment. In our multi-center study, TRG evaluation was performed by experienced pathologists at each participating institution according to standardized AJCC criteria, with assessments conducted in a blinded manner. However, the absence of centralized pathological review remains a limitation. We agree that emerging approaches, including digital pathology and artificial intelligence–assisted quantification, hold promise for improving the objectivity and reproducibility of residual tumor assessment, thereby enhancing the reliability of TRG as a stratification tool. Finally, we fully support the suggestion to integrate TRG with molecular biomarkers such as circulating tumor DNA (ctDNA) and PD-L1 expression [3, 4]. Although such data were not available in the present retrospective cohort, this represents an important direction for future investigation. Growing evidence indicates that combining pathological response with molecular profiling may provide a more refined framework for postoperative risk stratification and treatment selection. We are currently pursuing prospective studies incorporating serial biospecimen collection to explore the interplay between TRG and molecular markers in predicting long-term outcomes. In conclusion, we appreciate the recognition by Wu et al. of the clinical relevance of TRG in guiding postoperative management for esophageal squamous cell carcinoma. Their comments underscore the need for more granular therapeutic analyses, standardized and technology-enhanced pathological assessment, and integration of multi-dimensional biomarkers. These efforts will be essential to advance from conventional stratification toward a more precise and individualized treatment paradigm. Yizhou Huang: writing – original draft. Maohui Chen: writing – original draft. Chun Chen: writing – review and editing. Bin Zheng: writing – review and editing. The authors have nothing to report. The authors declare no conflicts of interest. This article is linked to Wu et al. paper. To view this article, visit https://doi.org/10.1002/cam4.71786. Data sharing not applicable to this article as no datasets were generated or analysed during the current study.