{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:21:22Z","timestamp":1772554882681,"version":"3.50.1"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"17","license":[{"start":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T00:00:00Z","timestamp":1657238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Major Inter-Disciplinary Research"},{"DOI":"10.13039\/501100001779","name":"Monash University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001779","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>The molecular subtyping of gastric cancer (adenocarcinoma) into four main subtypes based on integrated multiomics profiles, as proposed by The Cancer Genome Atlas (TCGA) initiative, represents an effective strategy for patient stratification. However, this approach requires the use of multiple technological platforms, and is quite expensive and time-consuming to perform. A computational approach that uses histopathological image data to infer molecular subtypes could be a practical, cost- and time-efficient complementary tool for prognostic and clinical management purposes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Here, we propose a deep learning ensemble approach (called DEMoS) capable of predicting the four recognized molecular subtypes of gastric cancer directly from histopathological images. DEMoS achieved tile-level area under the receiver-operating characteristic curve (AUROC) values of 0.785, 0.668, 0.762 and 0.811 for the prediction of these four subtypes of gastric cancer [i.e. (i) Epstein\u2013Barr (EBV)-infected, (ii) microsatellite instability (MSI), (iii) genomically stable (GS) and (iv) chromosomally unstable tumors (CIN)] using an independent test dataset, respectively. At the patient-level, it achieved AUROC values of 0.897, 0.764, 0.890 and 0.898, respectively. Thus, these four subtypes are well-predicted by DEMoS. Benchmarking experiments further suggest that DEMoS is able to achieve an improved classification performance for image-based subtyping and prevent model overfitting. This study highlights the feasibility of using a deep learning ensemble-based method to rapidly and reliably subtype gastric cancer (adenocarcinoma) solely using features from histopathological images.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>All whole slide images used in this study was collected from the TCGA database. This study builds upon our previously published HEAL framework, with related documentation and tutorials available at http:\/\/heal.erc.monash.edu.au. The source code and related models are freely accessible at https:\/\/github.com\/Docurdt\/DEMoS.git.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac456","type":"journal-article","created":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T13:18:32Z","timestamp":1657286312000},"page":"4206-4213","source":"Crossref","is-referenced-by-count":18,"title":["DEMoS: a deep learning-based ensemble approach for predicting the molecular subtypes of gastric adenocarcinomas from histopathological images"],"prefix":"10.1093","volume":"38","author":[{"given":"Yanan","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University , Melbourne 3800, Australia"}]},{"given":"Changyuan","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University , Melbourne 3800, Australia"}]},{"given":"Terry","family":"Kwok","sequence":"additional","affiliation":[{"name":"Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University , Melbourne 3800, Australia"}]},{"given":"Christopher A","family":"Bain","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Monash University , Melbourne 3800, Australia"}]},{"given":"Xiangyang","family":"Xue","sequence":"additional","affiliation":[{"name":"Department of General Surgery, The Second Affiliated Hospital of Wenzhou Medical University , Wenzhou, Zhejiang 325027, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4423-1690","authenticated-orcid":false,"given":"Robin B","family":"Gasser","sequence":"additional","affiliation":[{"name":"Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne , Parkville, VIC 3010, Australia"}]},{"given":"Geoffrey I","family":"Webb","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Monash Centre for Data Science, Monash University , Melbourne 3800, Australia"},{"name":"Department of Data Science and Artificial Intelligence, Monash University , Melbourne, VIC 3800, Australia"}]},{"given":"Alex","family":"Boussioutas","sequence":"additional","affiliation":[{"name":"The Alfred Hospital , Melbourne, VIC 3004, Australia"},{"name":"Central Clinical School, Monash University , Melbourne, VIC 3004, Australia"},{"name":"Department of Medicine, Royal Melbourne Hospital, University of Melbourne , Parkville, VIC 3010, Australia"}]},{"given":"Xian","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of General Surgery, The Second Affiliated Hospital of Wenzhou Medical University , Wenzhou, Zhejiang 325027, China"}]},{"given":"Roger J","family":"Daly","sequence":"additional","affiliation":[{"name":"Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University , Melbourne 3800, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8031-9086","authenticated-orcid":false,"given":"Jiangning","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University , Melbourne 3800, Australia"},{"name":"Department of Data Science and Artificial Intelligence, Monash University , Melbourne, VIC 3800, Australia"}]}],"member":"286","published-online":{"date-parts":[[2022,7,8]]},"reference":[{"key":"2023041408373300000_","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1038\/nature13480","article-title":"Comprehensive molecular characterization of gastric adenocarcinoma","volume":"513","author":"Bass","year":"2014","journal-title":"Nature"},{"key":"2023041408373300000_","doi-asserted-by":"crossref","first-page":"1786","DOI":"10.3390\/biom11121786","article-title":"xDEEP-MSI: explainable bias-rejecting microsatellite instability deep learning system in colorectal cancer","volume":"11","author":"Bustos","year":"2021","journal-title":"Biomolecules"},{"key":"2023041408373300000_","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1038\/s41591-019-0508-1","article-title":"Clinical-grade computational pathology using weakly supervised deep learning on whole slide images","volume":"25","author":"Campanella","year":"2019","journal-title":"Nat. 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