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 in | Scientific reports Vol. 12; no. 1; p. 18466 |
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Main Authors | , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
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London
Nature Publishing Group UK
02.11.2022
Nature Publishing Group Nature Portfolio |
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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. |
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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 |
Author_xml | – sequence: 1 givenname: Yeojin surname: Jeong fullname: Jeong, Yeojin organization: Genome & Health Data Lab, School of Public Health, Seoul National University – sequence: 2 givenname: Cristina Eunbee surname: Cho fullname: Cho, Cristina Eunbee organization: Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine – sequence: 3 givenname: Ji-Eon surname: Kim fullname: Kim, Ji-Eon organization: Wonkwang University Medical Research Convergence Center, Wonkwang University Hospital – sequence: 4 givenname: Jonghyun surname: Lee fullname: Lee, Jonghyun organization: Department of Medical and Digital Engineering, Hanyang University College of Engineering – sequence: 5 givenname: Namkug surname: Kim fullname: Kim, Namkug organization: Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine – sequence: 6 givenname: Woon Yong surname: Jung fullname: Jung, Woon Yong organization: Department of Pathology, Hanyang University Guri Hospital, Hanyang University College of Medicine – sequence: 7 givenname: Joohon surname: Sung fullname: Sung, Joohon organization: Genome & Health Data Lab, School of Public Health, Seoul National University – sequence: 8 givenname: Ju Han surname: Kim fullname: Kim, Ju Han organization: Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul National University Biomedical Informatics (SNUBI) – sequence: 9 givenname: Yoo Jin surname: Lee fullname: Lee, Yoo Jin organization: Department of Pathology, Korea University Anam Hospital, Korea University College of Medicine – sequence: 10 givenname: Jiyoon surname: Jung fullname: Jung, Jiyoon organization: Department of Pathology, Kangnam Sacred Heart Hospital, College of Medicine, Hallym University – sequence: 11 givenname: Juyeon surname: Pyo fullname: Pyo, Juyeon organization: Department of Pathology, International St. Mary’s Hospital, Catholic Kwandong University College of Medicine – sequence: 12 givenname: Jisun surname: Song fullname: Song, Jisun organization: Department of Pathology, Ewha Womans University Seoul Hospital, Ewha Womans University College of Medicine – sequence: 13 givenname: Jihwan surname: Park fullname: Park, Jihwan organization: School of Software Convergence, College of Software Convergence, Dankook University – sequence: 14 givenname: Kyoung Min surname: Moon fullname: Moon, Kyoung Min email: pulmogicu@ulsan.ac.kr organization: Department of Pulmonary, Allergy, and Critical Care Medicine, Gangneung Asan Hospital, College of Medicine, University of Ulsan – sequence: 15 givenname: Sangjeong surname: Ahn fullname: Ahn, Sangjeong email: vanitasahn@gmail.com organization: Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul National University Biomedical Informatics (SNUBI), Department of Pathology, Korea University Anam Hospital, Korea University College of Medicine |
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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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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 |
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