ArticleScientific reports2025
A robust artificial intelligence system for predicting EBV status in gastric cancer biopsy and resection specimens.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning prediction of MSI/dMMR, EBV, HER2, PD-L1, and CLDN18.2 status directly from hematoxylin and eosin-stained slides in gastric and gastroesophageal junction adenocarcinoma: a systematic review.Frontiers in oncology · 2026Pooled it
- Artificial Intelligence for Molecular Biomarker Identification in Gastrointestinal and Hepatobiliary Cancers.International journal of molecular sciences · 2026Review
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5 authors.
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Abstract
Epstein-Barr virus (EBV) associated gastric cancer, accounting for ~ 9% of all gastric cancers, has unique pathologic, genomic, and clinical features and is linked to a better prognosis. Therefore, we aim to develop and validate a robust deep learning-based classifier to detect EBV positivity for both biopsy and surgical resection specimens in an accurate and reliable manner. We propose a two-stage stain normalization-based robust artificial intelligence classifier for EBV-gastric cancer positivity detection (EBV-TRACER). The results were aggregated to compute three prediction scores including EBV positive cancer-to-tissue ratio, EBV positive cancer-to-tumor ratio, and EBV positive cancer size. To assess the classification performance, we measured the area under the receiver operating curve for detecting EBV status. We also conducted nuclei segmentation and classification to quantitatively analyze and visualize the relationship between the EBV status and lymphocyte features. Our analysis included 2684 gastric specimens with diagnostic clinical reports for gastric cancer collected from January 1, 2011 to December 31, 2023. In the internal validation cohorts, EBV-TRACER yielded AUCs ranging from 0.6596 (95% CI: 0.5359-0.7697) to 0.8414 (95% CI: 0.7267-0.9263) using the three scores. In the external validation cohort, AUCs of 0.7644 (95% CI: 0.6829-0.8399), 0.7652 (95% CI: 0.6850-0.8398), and 0.7221 (95% CI: 0.6134-0.8178) were obtained for the three scores, respectively. Overall, EBV-TRACER significantly outperforms models without stain normalization and those using conventional stain normalization. It could serve as a promising tool for improving the efficiency and accuracy of decision making in gastric cancer diagnosis, supporting more effective treatment planning and strategy development.
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