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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 em…

By the Truvace desk1 min read1 sourcePeer-reviewedTRV-2026-0984· cite & verify
Artificial intelligence-driven decision-making after endoscopic resection for early gastric cancer
SistemaColetorDeSecre escJPG by Robertolyra at Portuguese Wikipedia. CC BY-SA 3.0 · http://creativecommons.org/licenses/by-sa/3.0/

In brief

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.

Main points

  1. 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.
  2. 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.
  3. 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.

The problem

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

Sources

  1. Peer-reviewedClinical Endoscopy2026-09-04

The debate