Artificial intelligence-driven decision-making after endoscopic resection for early gastric cancer

Early gastric cancer (EGC) is increasingly managed by endoscopic resection (ER); however, lymph node metastasis (LNM), which occurs in approximately 5%-10% of cases, remains the key determinant for recommending additional gastrectomy. Current guideline-based strategies, including the eCura system, provide structured risk stratification but rely on categorical decision-making and may lead to overtreatment, as nearly 90% of patients undergoing additional surgery do not have LNM. Artificial intelligence (AI) has emerged as a promising tool for improving LNM predictions. Machine learning models using clinicopathological variables have demonstrated promising discriminatory performance (area under the curve, 0.69-0.94), often outperforming conventional scoring systems such as eCura. However, these approaches rely on predefined variables and are susceptible to interobserver variability in pathological assessments. Whole-slide image-based AI directly analyzes histopathological images, offering an objective and reproducible approach. Although evidence for EGC is limited, recent multicenter data have demonstrated promising results using routine hematoxylin and eosin-stained slides. Beyond the LNM risk, treatment decisions should also consider patient factors such as age, comorbidities, and competing mortality risks. AI is expected to support treatment decision-making by integrating these multidimensional factors, enabling more personalized and risk-adapted management after ER.