− HEALTH Background Injuries are responsible for 950,000 deaths per year among children and adolescents under 18 years…− HEALTH Many problems in biomedicine can be posed as binary classification. When they are addressed using artificial…+ HEALTH Purpose The purpose of this study was to evaluate the performance of two general-use artificial intelligence…− HEALTH Early gastric cancer (EGC) is increasingly managed by endoscopic resection (ER); however, lymph node metastas…− LABOR Traditional secondary meta-analysis workflows are highly labour-intensive, time-consuming, and difficult to u…− HEALTH There is a growing literature on the prediction of risk of deterioration in hospital settings, including by l…− SCIENCE Self-driving cars increasingly rely on deep neural networks to achieve human-like driving 1-3 . The opacity o…+ HEALTH Objective This study aimed to develop a mitochondrial and hematopoiesis-related differentially expressed gene…
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Health·P Space·Evidence-backed problem·Published 2026-09-05

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

Abstract: 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…

TRV-2026-0984Peer-reviewedPermanent record — 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/

The quick read

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
  • 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.
  • 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.
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.

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