Deep learning model to predict Epstein–Barr virus associated gastric cancer in histology

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-bas...

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Published inScientific reports Vol. 12; no. 1; p. 18466
Main Authors Jeong, Yeojin, Cho, Cristina Eunbee, Kim, Ji-Eon, Lee, Jonghyun, Kim, Namkug, Jung, Woon Yong, Sung, Joohon, Kim, Ju Han, Lee, Yoo Jin, Jung, Jiyoon, Pyo, Juyeon, Song, Jisun, Park, Jihwan, Moon, Kyoung Min, Ahn, Sangjeong
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 02.11.2022
Nature Publishing Group
Nature Portfolio
Subjects
631/67/1504/1829
631/67/2322
692/53/2423
Datasets
Decision making
Deep Learning
Epstein-Barr virus
Epstein-Barr Virus Infections - genetics
Gastric cancer
Genomes
Herpesvirus 4, Human - genetics
Histology
Humanities and Social Sciences
Humans
multidisciplinary
Patients
Prognosis
Science
Science (multidisciplinary)
Stomach Neoplasms - pathology
Tumors
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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.
AbstractList 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.
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.
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.
ArticleNumber 18466
Author Cho, Cristina Eunbee
Kim, Namkug
Kim, Ju Han
Jung, Woon Yong
Lee, Jonghyun
Sung, Joohon
Park, Jihwan
Lee, Yoo Jin
Jung, Jiyoon
Song, Jisun
Pyo, Juyeon
Moon, Kyoung Min
Kim, Ji-Eon
Jeong, Yeojin
Ahn, Sangjeong
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Snippet The detection of Epstein–Barr virus (EBV) in gastric cancer patients is crucial for clinical decision making, as it is related with specific treatment...
The detection of Epstein-Barr virus (EBV) in gastric cancer patients is crucial for clinical decision making, as it is related with specific treatment...
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...
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...
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StartPage 18466
SubjectTerms 631/67/1504/1829
631/67/2322
692/53/2423
Datasets
Decision making
Deep Learning
Epstein-Barr virus
Epstein-Barr Virus Infections - genetics
Gastric cancer
Genomes
Herpesvirus 4, Human - genetics
Histology
Humanities and Social Sciences
Humans
multidisciplinary
Patients
Prognosis
Science
Science (multidisciplinary)
Stomach Neoplasms - pathology
Tumors
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Title Deep learning model to predict Epstein–Barr virus associated gastric cancer in histology
URI https://link.springer.com/article/10.1038/s41598-022-22731-x
https://www.ncbi.nlm.nih.gov/pubmed/36323712
https://www.proquest.com/docview/2731320062
https://search.proquest.com/docview/2731721002
https://pubmed.ncbi.nlm.nih.gov/PMC9630260
https://doaj.org/article/9eac9b86cc1f4800b8449331db9e5991
Volume 12
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