− 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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TRV-2026-0984Version 1 · Certified

Written 2026-09-05 06:05:34 UTC · current record

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TRUVACE RECORD VERSION
record: TRV-2026-0984
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-05T06:05:34.543765Z
status: published
lens: p_space
sector: health
headline: Artificial intelligence-driven decision-making after endoscopic resection for early gastric cancer
dek: 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…
gain_title: (none)
problem_title: 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.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: 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.
problem_evidence: (none)
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.
limitation: 
tag: Evidence-backed problem
key_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.
rundown: 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.
sources:
- peer_reviewed | Clinical Endoscopy | https://doi.org/10.5946/ce.2026.176 | 2026-09-04
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