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Deep learning model to predict Epstein–Barr virus associated gastric cancer in histology
- Title
- Deep learning model to predict Epstein–Barr virus associated gastric cancer in histology
- Authors
- Jeong Y.; Cho C.E.; Kim J.-E.; Lee J.; Kim N.; Jung W.Y.; Sung J.; Kim J.H.; Lee Y.J.; Jung J.; Pyo J.; Song J.; Park J.; Moon K.M.; Ahn S.
- Ewha Authors
- 송지선
- SCOPUS Author ID
- 송지선
- Issue Date
- 2022
- Journal Title
- Scientific Reports
- ISSN
- 2045-2322
- Citation
- Scientific Reports vol. 12, no. 1
- Publisher
- Nature Research
- Indexed
- SCIE; SCOPUS
- Document Type
- Article
- Abstract
- The detection of Epstein–Barr virus (EBV) in gastric cancer patients is crucial for clinical decision making, as it is related with specific treatment responses and prognoses. Despite its importance, the limited medical resources preclude universal EBV testing. Herein, we propose a deep learning-based EBV prediction method from H&E-stained whole-slide images (WSI). Our model was developed using 319 H&E stained WSI (26 EBV positive; TCGA dataset) from the Cancer Genome Atlas, and 108 WSI (8 EBV positive; ISH dataset) from an independent institution. Our deep learning model, EBVNet consists of two sequential components: a tumor classifier and an EBV classifier. We visualized the learned representation by the classifiers using UMAP. We externally validated the model using 60 additional WSI (7 being EBV positive; HGH dataset). We compared the model’s performance with those of four pathologists. EBVNet achieved an AUPRC of 0.65, whereas the four pathologists yielded a mean AUPRC of 0.41. Moreover, EBVNet achieved an negative predictive value, sensitivity, specificity, precision, and F1-score of 0.98, 0.86, 0.92, 0.60, and 0.71, respectively. Our proposed model is expected to contribute to prescreen patients for confirmatory testing, potentially to save test-related cost and labor. © 2022, The Author(s).
- DOI
- 10.1038/s41598-022-22731-x
- Appears in Collections:
- 의료원 > 의료원 > Journal papers
- Files in This Item:
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s41598-022-22731-x.pdf(1.94 MB)
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