ArticleFrontiers in medicine2026
Endostrat: a multicenter study of AI-assisted two-level risk stratification for gastric precancerous and early neoplastic lesion screening.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: In large-scale endoscopic screening, neoplastic lesions constitute only a small fraction of the cases examined, making detection susceptible to reader fatigue. Most existing AI systems rely on image-level annotations and single-stage classification, limiting patient-level assessment and safety-aware review prioritization. We developed and validated EndoStrat, a two-stage AI-assisted risk stratification system that triages low-risk patients and prioritizes high-risk patients for focused endoscopic review. Methods: This retrospective, multicenter study included 8,829 patients (approximately 220,000 white-light endoscopic images) from three centers with distinct pathological compositions. Patients were classified into four categories: normal/superficial gastritis, atrophic gastritis/intestinal metaplasia (Atrophy/IM), low-grade intraepithelial neoplasia (LGIN), and high-grade intraepithelial neoplasia/early gastric cancer (HGIN/EGC). Center 1 was split into a training cohort ( Results: Across the three validation cohorts, Level 1 achieved AUCs of 0.770, 0.720, and 0.674, which were numerically higher than those of both comparators. At the exact fixed operating threshold of 0.22, the HGIN/EGC flag rate was 93.0% or higher in all cohorts, and the neoplasia miss rate ranged from 5.8% to 8.1%. Among patients with normal/superficial pathology, 21.1%-26.1% were classified as low risk; across all patients, the corresponding overall low-risk assignment rates ranged from 11.2% to 19.6%. In a module-level analysis including all patients with pathology other than Normal/Superficial irrespective of Level-1 classification, the Level-2 continuous risk score showed a clear monotonic gradient across Atrophy/IM, LGIN, and HGIN/EGC (all Conclusions: EndoStrat showed consistent risk-stratification performance across three validation cohorts with different pathological compositions. These retrospective findings suggest that EndoStrat may have potential to support risk-based review prioritization while retaining a high proportion of neoplastic cases in the review-required group. Prospective multicenter studies are needed to determine its effects on clinical workflow, diagnostic performance, and patient outcomes.
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