Machine learning approaches to early detection of delayed wound healing following gastric cancer surgery

Abstract

Delayed wound healing following radical gastrectomy remains an important yet underappreciated complication that prolongs hospitalization, increases costs, and undermines patient recovery. In An et al’s recent study, the authors present a machine learning-based risk prediction approach using routinely available clinical and laboratory parameters. Among the evaluated algorithms, a decision tree model demonstrated excellent discrimination, achieving an area under the curve of 0.951 in the validation set and notably identifying all true cases of delayed wound healing at the Youden index threshold. The inclusion of variables such as drainage duration, preoperative white blood cell and neutrophil counts, alongside age and sex, highlights the pragmatic appeal of the model for early postoperative monitoring. Nevertheless, several aspects warrant critical reflection, including the reliance on a postoperative variable (drainage duration), internal validation only, and certain reporting inconsistencies. This letter underscores both the promise and the limitations of adopting interpretable machine learning models in perioperative care. We advocate for transparent reporting, external validation, and careful consideration of clinically actionable timepoints before integration into practice. Ultimately, this work represents a valuable step toward precision risk stratification in gastric cancer surgery, and sets the stage for multicenter, prospective evaluations.

Keywords: Gastric cancer; Radical gastrectomy; Delayed wound healing; Machine learning; Decision tree; Risk prediction

Core Tip: This letter highlights the potential of machine learning models in predicting delayed wound healing after radical gastrectomy. By leveraging routinely available clinical and laboratory parameters, interpretable models such as decision trees may support early risk stratification and postoperative monitoring. However, external validation and the use of pre- or intraoperative variables are essential before widespread clinical adoption.