{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T17:31:40Z","timestamp":1793295100601,"version":"4.5.0"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T00:00:00Z","timestamp":1757548800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T00:00:00Z","timestamp":1757548800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Abdom Radiol"],"DOI":"10.1007\/s00261-025-05181-7","type":"journal-article","created":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T04:28:40Z","timestamp":1757564920000},"page":"1694-1710","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Artificial intelligence in gastric cancer: a systematic review of machine learning and deep learning applications"],"prefix":"10.1007","volume":"51","author":[{"given":"Muna","family":"Alsallal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marwah Shaker","family":"Habeeb","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krunal","family":"Vaghela","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H.","family":"Malathi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alisha","family":"Vashisht","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Prabhat Kumar","family":"Sahu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Devendra","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali Fawzi","family":"Al-Hussainy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Idris A.","family":"Aljanaby","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hayder Naji","family":"Sameer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zainab H.","family":"Athab","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohaned","family":"Adil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed","family":"Yaseen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bagher","family":"Farhood","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,11]]},"reference":[{"issue":"25","key":"5181_CR1","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.ebiom.2017.10.014","volume":"25","author":"S Shichijo","year":"2017","unstructured":"Shichijo S, Nomura S, Aoyama K. Application of convolutional neural networks in the diagnosis of Helicobacter pylori infection based on endoscopic images. EBioMedicine. 2017;25(25):106\u2013111.","journal-title":"EBioMedicine"},{"issue":"4","key":"5181_CR2","first-page":"462","volume":"31","author":"H Nakashima","year":"2018","unstructured":"Nakashima H, Kawahira H, Kawachi H. Artificial intelligence diagnosis of Helicobacter pylori infection using blue laser imaging\u2013bright and linked color imaging: a single\u2013center prospective study. Ann Gastroenterol. 2018;31(4):462\u2013468.","journal-title":"Ann Gastroenterol"},{"issue":"3","key":"5181_CR3","first-page":"209","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians. 2021;71(3):209\u201349.","journal-title":"CA: a cancer journal for clinicians"},{"issue":"1","key":"5181_CR4","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1136\/gutjnl-2019-319347","volume":"69","author":"P Guimar\u00e3es","year":"2020","unstructured":"Guimar\u00e3es P, Keller A, Fehlmann T. Deep\u2013learning based detection of gastric precancerous conditions. Gut. 2020;69(1):4\u20136.","journal-title":"Gut"},{"issue":"5","key":"5181_CR5","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1016\/j.dld.2019.12.146","volume":"52","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Li F, Yuan F. Diagnosing chronic atrophic gastritis by gastroscopy using artificial intelligence. Dig Liver Dis. 2020;52(5):566\u2013572.","journal-title":"Dig Liver Dis"},{"key":"5181_CR6","doi-asserted-by":"publisher","first-page":"104026","DOI":"10.1016\/j.compbiomed.2020.104026","volume":"126","author":"T Yan","year":"2020","unstructured":"Yan T, Wong PK, Choi IC. Intelligent diagnosis of gastric intestinal metaplasia based on convolutional neural network and limited number of endoscopic images. Comput Biol Med. 2020;126:104026.","journal-title":"Comput Biol Med"},{"issue":"4","key":"5181_CR7","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1007\/s10120-018-0793-2","volume":"21","author":"T Hirasawa","year":"2018","unstructured":"Hirasawa T, Aoyama K, Tanimoto T. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer. 2018;21(4):653\u2013660.","journal-title":"Gastric Cancer"},{"issue":"11","key":"5181_CR8","doi-asserted-by":"publisher","first-page":"1645","DOI":"10.1016\/S1470-2045(19)30637-0","volume":"20","author":"H Luo","year":"2019","unstructured":"Luo H, Xu G, Li C, et al. Real\u2013time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case\u2013control, diagnostic study. Lancet Oncol. 2019;20(11):1645\u20131654.","journal-title":"Lancet Oncol"},{"key":"5181_CR9","doi-asserted-by":"crossref","unstructured":"Hsu CC, Ma HT, Lee JY. SSSNet: small\u2013scale\u2013aware siamese network for gastric cancer detection. In: Proc IEEE 16th Int Conf on Advanced Video and Signal\u2013Based Surveillance (AVSS); 2019. p. 1\u20135.","DOI":"10.1109\/AVSS.2019.8909849"},{"issue":"8","key":"5181_CR10","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.3390\/jcm8091310","volume":"8","author":"HJ Yoon","year":"2019","unstructured":"Yoon HJ, Kim S, Kim J\u2013H. A lesion\u2013based convolutional neural network improves endoscopic detection and depth prediction of early gastric cancer. J Clin Med. 2019;8(8):1310.","journal-title":"J Clin Med"},{"issue":"8","key":"5181_CR11","doi-asserted-by":"publisher","first-page":"1767","DOI":"10.1093\/annonc\/mdx051","volume":"28","author":"F Lordick","year":"2017","unstructured":"Lordick F, Shitara K, Janjigian YY. New agents on the horizon in gastric cancer. Annals of Oncology. 2017;28(8):1767\u201375.","journal-title":"Annals of Oncology"},{"issue":"21","key":"5181_CR12","doi-asserted-by":"publisher","first-page":"5982","DOI":"10.3390\/s20215982","volume":"20","author":"DT Nguyen","year":"2020","unstructured":"Nguyen DT, Lee MB, Pham TD. Enhanced image\u2013based endoscopic pathological site classification using an ensemble of deep learning models. Sensors (Basel). 2020;20(21):5982.","journal-title":"Sensors (Basel)"},{"issue":"6","key":"5181_CR13","doi-asserted-by":"publisher","first-page":"1333","DOI":"10.1016\/j.gie.2020.11.014","volume":"93","author":"H Hu","year":"2021","unstructured":"Hu H, Gong L, Dong D. Identifying early gastric cancer under magnifying narrow\u2013band images with deep learning: a multicenter study. Gastrointest Endosc. 2021;93(6):1333\u20131341.e3.","journal-title":"Gastrointest Endosc"},{"issue":"4","key":"5181_CR14","doi-asserted-by":"publisher","first-page":"806","DOI":"10.1016\/j.gie.2018.11.011","volume":"89","author":"Y Zhu","year":"2019","unstructured":"Zhu Y, Wang QC, Xu MD. Application of convolutional neural network in the diagnosis of the invasion depth of gastric cancer based on conventional endoscopy. Gastrointest Endosc. 2019;89(4):806\u2013815.e1.","journal-title":"Gastrointest Endosc"},{"issue":"4","key":"5181_CR15","doi-asserted-by":"publisher","first-page":"866","DOI":"10.1016\/j.gie.2020.06.047","volume":"92","author":"S Nagao","year":"2020","unstructured":"Nagao S, Tsuji Y, Sakaguchi Y, et al. Highly accurate artificial intelligence systems to predict the invasion depth of gastric cancer using white\u2013light, narrow\u2013band and indigo\u2013carmine dye contrast imaging. Gastrointest Endosc. 2020;92(4):866\u2013873.e1.","journal-title":"Gastrointest Endosc"},{"issue":"4","key":"5181_CR16","doi-asserted-by":"publisher","first-page":"884","DOI":"10.1007\/s10120-020-01071-7","volume":"23","author":"P An","year":"2020","unstructured":"An P, Yang D, Wang J. A deep learning method for delineating early gastric cancer resection margin under chromoendoscopy and white light endoscopy. Gastric Cancer. 2020;23(4):884\u2013892.","journal-title":"Gastric Cancer"},{"issue":"5","key":"5181_CR17","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1055\/a-1229-0920","volume":"53","author":"T Ling","year":"2021","unstructured":"Ling T, Wu L, Fu Y, et al. A deep learning\u2013based system for identifying differentiation status and delineating margins of early gastric cancer in magnifying narrow\u2013band imaging endoscopy. Endoscopy. 2021;53(5):469\u2013477.","journal-title":"Endoscopy"},{"issue":"1","key":"5181_CR18","doi-asserted-by":"publisher","first-page":"7847","DOI":"10.1038\/s41598-024-58361-8","volume":"14","author":"K Zhang","year":"2024","unstructured":"Zhang K, Wang H, Cheng Y, Liu H, Gong Q, Zeng Q, et al. Early gastric cancer detection and lesion segmentation based on deep learning and gastroscopic images. Sci Rep. 2024;14(1):7847.","journal-title":"Sci Rep"},{"issue":"3","key":"5181_CR19","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1007\/s10120-024-01469-7","volume":"27","author":"S Suzuki","year":"2024","unstructured":"Suzuki S, Monno Y, Arai R, Miyaoka M, Toya Y, Esaki M, et al. Diagnostic performance of deep\u2013learning\u2013based virtual chromoendoscopy in gastric neoplasms. Gastric Cancer. 2024;27(3):539\u2013547.","journal-title":"Gastric Cancer"},{"issue":"36","key":"5181_CR20","doi-asserted-by":"publisher","first-page":"5408","DOI":"10.3748\/wjg.v26.i36.5408","volume":"26","author":"PH Niu","year":"2020","unstructured":"Niu PH, Zhao LL, Wu HL, Zhao DB, Chen YT. Artificial intelligence in gastric cancer: application and future perspectives. World J Gastroenterol. 2020;26(36):5408\u20135419.","journal-title":"World J Gastroenterol"},{"issue":"7","key":"5181_CR21","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1055\/s-2004-814519","volume":"36","author":"CR Huang","year":"2004","unstructured":"Huang CR, Sheu BS, Chung PC. Computerized diagnosis of Helicobacter pylori infection and associated gastric inflammation from endoscopic images using a neural network with feature selection. Endoscopy. 2004;36(7):601\u2013608.","journal-title":"Endoscopy"},{"issue":"4","key":"5181_CR22","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1111\/den.13509","volume":"32","author":"T Yasuda","year":"2020","unstructured":"Yasuda T, Hiroyasu T, Hiwa S. Potential of automatic diagnosis system with linked color imaging for diagnosis of Helicobacter pylori infection. Dig Endosc. 2020;32(4):373\u2013381.","journal-title":"Dig Endosc"},{"issue":"5","key":"5181_CR23","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1111\/jgh.12149","volume":"28","author":"R Miyaki","year":"2013","unstructured":"Miyaki R, Yoshida S, Tanaka S, et al. Quantitative identification of mucosal gastric cancer under magnifying endoscopy with flexible spectral imaging color enhancement. J Gastroenterol Hepatol. 2013;28(5):841\u2013847.","journal-title":"J Gastroenterol Hepatol"},{"issue":"2","key":"5181_CR24","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1097\/MCG.0000000000000104","volume":"49","author":"R Miyaki","year":"2015","unstructured":"Miyaki R, Yoshida S, Tanaka S. A computer system to be used with laser\u2013based endoscopy for quantitative diagnosis of early gastric cancer. J Clin Gastroenterol. 2015;49(2):108\u2013115.","journal-title":"J Clin Gastroenterol"},{"issue":"8","key":"5181_CR25","doi-asserted-by":"publisher","first-page":"1077","DOI":"10.1016\/j.acra.2019.10.020","volume":"27","author":"Y Wang","year":"2020","unstructured":"Wang Y, Liu W, Yu Y. Prediction of the depth of tumor invasion in gastric cancer: potential role of CT radiomics. Acad Radiol. 2020;27(8):1077\u20131084.","journal-title":"Acad Radiol"},{"key":"5181_CR26","doi-asserted-by":"publisher","first-page":"109277","DOI":"10.1016\/j.ejrad.2020.109277","volume":"132","author":"RJ Sun","year":"2020","unstructured":"Sun RJ, Fang MJ, Tang L, et al. CT\u2013based deep learning radiomics analysis for evaluation of serosa invasion in advanced gastric cancer. Eur J Radiol. 2020;132:109277.","journal-title":"Eur J Radiol"},{"issue":"7","key":"5181_CR27","doi-asserted-by":"publisher","first-page":"912","DOI":"10.1016\/j.annonc.2020.04.003","volume":"31","author":"D Dong","year":"2020","unstructured":"Dong D, Fang MJ, Tang L, et al. Deep learning radiomic nomogram can predict lymph node metastasis in locally advanced gastric cancer: an international multicenter study. Ann Oncol. 2020;31(7):912\u2013920.","journal-title":"Ann Oncol"},{"issue":"6","key":"5181_CR28","doi-asserted-by":"publisher","first-page":"2324","DOI":"10.1007\/s00330-019-06621-x","volume":"30","author":"J Li","year":"2020","unstructured":"Li J, Dong D, Fang M, et al. Dual\u2013energy CT\u2013based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer. Eur Radiol. 2020;30(6):2324\u20132333.","journal-title":"Eur Radiol"},{"issue":"5","key":"5181_CR29","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1002\/bjs.11928","volume":"108","author":"C Jin","year":"2021","unstructured":"Jin C, Jiang Y, Yu H, et al. Deep learning analysis of the primary tumour and prediction of lymph node metastasis in gastric cancer. Br J Surg. 2021;108(5):542\u2013549.","journal-title":"Br J Surg"},{"issue":"3","key":"5181_CR30","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1093\/annonc\/mdz001","volume":"30","author":"D Dong","year":"2019","unstructured":"Dong D, Tang L, Li ZY, et al. Development and validation of an individualized nomogram to identify occult peritoneal metastasis in advanced gastric cancer. Ann Oncol. 2019;30(3):431\u2013438.","journal-title":"Ann Oncol"},{"key":"5181_CR31","doi-asserted-by":"publisher","first-page":"601869","DOI":"10.3389\/fonc.2020.601869","volume":"10","author":"Z Huang","year":"2020","unstructured":"Huang Z, Liu D, Chen X, et al. Deep convolutional neural network based on CT images for preoperative diagnosis of occult peritoneal metastasis in advanced gastric cancer. Front Oncol. 2020;10:601869.","journal-title":"Front Oncol"},{"issue":"11","key":"5181_CR32","doi-asserted-by":"publisher","first-page":"e2032269","DOI":"10.1001\/jamanetworkopen.2020.32269","volume":"4","author":"Y Jiang","year":"2021","unstructured":"Jiang Y, Liang X, Wang W, et al. Noninvasive prediction of occult peritoneal metastasis in gastric cancer using deep learning. JAMA Netw Open. 2021;4(11):e2032269.","journal-title":"JAMA Netw Open"},{"issue":"6","key":"5181_CR33","doi-asserted-by":"publisher","first-page":"641","DOI":"10.3390\/cancers11050641","volume":"11","author":"YC Lai","year":"2019","unstructured":"Lai YC, Yeh TS, Wu RC, et al. Acute tumor transition angle on CT predicts chromosomal instability status of primary gastric cancer: radiogenomics analysis from TCGA and independent validation. Cancers (Basel). 2019;11(6):641.","journal-title":"Cancers (Basel)"},{"issue":"150","key":"5181_CR34","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.radonc.2020.06.010","volume":"150","author":"L Zhang","year":"2020","unstructured":"Zhang L, Dong D, Zhang W. A deep learning risk prediction model for overall survival in gastric cancer: a multicenter study. Radiother Oncol. 2020;150(150):73\u201380.","journal-title":"Radiother Oncol"},{"issue":"5","key":"5181_CR35","doi-asserted-by":"publisher","first-page":"e1153","DOI":"10.1097\/SLA.0000000000003778","volume":"274","author":"Y Jiang","year":"2021","unstructured":"Jiang Y, Jin C, Yu H, et al. Development and validation of a deep learning CT signature to predict survival and chemotherapy benefit in gastric cancer: a multicenter retrospective study. Ann Surg. 2021;274(5):e1153\u2013e1161.","journal-title":"Ann Surg"},{"issue":"24","key":"5181_CR36","doi-asserted-by":"publisher","first-page":"7224","DOI":"10.7150\/jca.46704","volume":"11","author":"JW Tan","year":"2020","unstructured":"Tan JW, Wang L, Chen Y, et al. Predicting chemotherapeutic response for far\u2013advanced gastric cancer using radiomics with deep learning semi\u2013automatic segmentation. J Cancer. 2020;11(24):7224\u20137236.","journal-title":"J Cancer"},{"key":"5181_CR37","doi-asserted-by":"publisher","first-page":"101834","DOI":"10.1016\/j.eclinm.2023.101834","volume":"57","author":"L Yuan","year":"2023","unstructured":"Yuan L, Yang L, Zhang S, Xu Z, Qin J, Shi Y, et al. Development of a tongue image\u2013based machine learning tool for diagnosis of gastric cancer: a prospective multicentre clinical cohort study. EClinicalMedicine. 2023;57:101834.","journal-title":"EClinicalMedicine"},{"key":"5181_CR38","doi-asserted-by":"publisher","first-page":"7598","DOI":"10.1097\/JS9.0000000000001627","volume":"110","author":"M Cao","year":"2024","unstructured":"Cao M, Hu C, Li F, He J, Li E, Zhang R, et al. Development and validation of a deep learning model for predicting gastric cancer recurrence based on CT imaging: a multicenter study. Int J Surg. 2024;110:7598\u20137606.","journal-title":"Int J Surg"},{"issue":"4","key":"5181_CR39","doi-asserted-by":"publisher","first-page":"881","DOI":"10.1016\/j.clnu.2024.02.005","volume":"43","author":"W Huang","year":"2024","unstructured":"Huang W, Wang C, Wang Y, Yu Z, Wang S, Yang J, et al. Predicting malnutrition in gastric cancer patients using CT\u2013derived deep learning features and clinical data. Clin Nutr. 2024;43(4):881\u2013891.","journal-title":"Clin Nutr"},{"key":"5181_CR40","doi-asserted-by":"publisher","first-page":"e56851","DOI":"10.2196\/56851","volume":"26","author":"J Tao","year":"2024","unstructured":"Tao J, Liu D, Hu FB, Zhang X, Yin H, Zhang H, et al. Development and validation of a CT\u2013based model for noninvasive prediction of the T stage in gastric cancer: multicenter retrospective study. J Med Internet Res. 2024;26:e56851.","journal-title":"J Med Internet Res"},{"key":"5181_CR41","first-page":"1","volume":"24","author":"C Hu","year":"2025","unstructured":"Hu C, Xia Y, Zheng Z, Cao M, Zheng G, Chen S, Sun J, Chen W, Zheng Q, Pan S, Zhang Y. AI-based large-scale screening of gastric cancer from noncontrast CT imaging. Nature Medicine. 2025 Jun 24:1\u20139.","journal-title":"Nature Medicine"},{"key":"5181_CR42","doi-asserted-by":"crossref","unstructured":"Li Y, Li X, Xie X. Deep learning based gastric cancer identification. In: Proc IEEE 15th Int Symp Biomed Imaging (ISBI);2018:182\u2013185.","DOI":"10.1109\/ISBI.2018.8363550"},{"key":"5181_CR43","doi-asserted-by":"crossref","unstructured":"Li Y, Xie X, Liu S, et al. GT\u2013Net: a deep learning network for gastric tumor diagnosis. In: Proc IEEE 30th Int Conf Tools with Artificial Intelligence (ICTAI);2018:20\u201324.","DOI":"10.1109\/ICTAI.2018.00014"},{"key":"5181_CR44","doi-asserted-by":"publisher","first-page":"75530","DOI":"10.1109\/ACCESS.2019.2918800","volume":"7","author":"M Sun","year":"2019","unstructured":"Sun M, Zhang G, Dang H, et al. Accurate gastric cancer segmentation in digital pathology images using deformable convolution and multi\u2013scale embedding networks. IEEE Access. 2019;7:75530\u201375541.","journal-title":"IEEE Access"},{"key":"5181_CR45","doi-asserted-by":"publisher","first-page":"101549","DOI":"10.1016\/j.media.2019.101549","volume":"58","author":"S Wang","year":"2019","unstructured":"Wang S, Zhu Y, Yu L. RMDL: recalibrated multi\u2013instance deep learning for whole slide gastric image classification. Med Image Anal. 2019;58:101549.","journal-title":"Med Image Anal"},{"key":"5181_CR46","doi-asserted-by":"crossref","unstructured":"Sharma H, Zerbe N, Heim D, et al. Cell nuclei attributed relational graphs for efficient representation and classification of gastric cancer in digital histopathology. In: Proc Medical Imaging 2016: Digital Pathology;2016:97910X.","DOI":"10.1117\/12.2216843"},{"key":"5181_CR47","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1016\/j.compmedimag.2017.06.001","volume":"61","author":"H Sharma","year":"2017","unstructured":"Sharma H, Zerbe N, Klempert I, et al. Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology. Comput Med Imaging Graph. 2017;61:2\u201313.","journal-title":"Comput Med Imaging Graph"},{"key":"5181_CR48","doi-asserted-by":"publisher","first-page":"685992","DOI":"10.3389\/fimmu.2021.685992","volume":"12","author":"Y Chen","year":"2021","unstructured":"Chen Y, Sun Z, Chen W, et al. The immune subtypes and landscape of gastric cancer and to predict based on whole\u2013slide images using deep learning. Front Immunol. 2021;12:685992.","journal-title":"Front Immunol"},{"issue":"7","key":"5181_CR49","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1038\/s41591-019-0462-y","volume":"25","author":"JN Kather","year":"2019","unstructured":"Kather JN, Pearson AT, Halama N, et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med. 2019;25(7):1054\u20131056.","journal-title":"Nat Med"},{"issue":"12","key":"5181_CR50","doi-asserted-by":"publisher","first-page":"3687","DOI":"10.3390\/cancers12123687","volume":"12","author":"R Valieris","year":"2020","unstructured":"Valieris R, Amaro L, Os\u00f3rio C, et al. Deep learning predicts underlying features on pathology images with therapeutic relevance for breast and gastric cancer. Cancers (Basel). 2020;12(12):3687.","journal-title":"Cancers (Basel)"},{"issue":"4","key":"5181_CR51","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1002\/cjp2.170","volume":"6","author":"A Meier","year":"2020","unstructured":"Meier A, Nekolla K, Hewitt LC, et al. Hypothesis\u2013free deep survival learning applied to the tumour microenvironment in gastric cancer. J Pathol Clin Res. 2020;6(4):273\u2013282.","journal-title":"J Pathol Clin Res"},{"key":"5181_CR52","doi-asserted-by":"crossref","first-page":"1639","DOI":"10.1038\/s41467-021-21975-x","volume":"12","author":"X Wang","year":"2021","unstructured":"Wang X, Chen Y, Gao Y, et al. Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning. Nat Commun. 2021;12:1639.","journal-title":"Nat Commun"},{"key":"5181_CR53","doi-asserted-by":"publisher","first-page":"101080","DOI":"10.1016\/j.drup.2024.101080","volume":"74","author":"G Che","year":"2024","unstructured":"Che G, Yin J, Wang W, Luo Y, Chen Y, Yu X, et al. Circumventing drug resistance in gastric cancer: a spatial multi\u2013omics exploration of chemo\u2013 and immunotherapeutic response dynamics. Drug Resist Updat. 2024;74:101080.","journal-title":"Drug Resist Updat"},{"key":"5181_CR54","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1186\/s12967-024-05262-z","volume":"22","author":"Y Liu","year":"2024","unstructured":"Liu Y, Chen W, Ruan R, Zhang Z, Wang Z, Guan T, et al. Deep learning based digital pathology for predicting treatment response to first\u2013line PD\u20131 blockade in advanced gastric cancer. J Transl Med. 2024;22:438.","journal-title":"J Transl Med"},{"issue":"2","key":"5181_CR55","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1007\/s10120-023-01451-9","volume":"27","author":"S Fang","year":"2024","unstructured":"Fang S, Liu Z, Qiu Q, Tang Z, Yang Y, Kuang Z, et al. Diagnosing and grading gastric atrophy and intestinal metaplasia using semi\u2013supervised deep learning on pathological images: development and validation study. Gastric Cancer. 2024;27(2):343\u2013354.","journal-title":"Gastric Cancer"},{"issue":"13","key":"5181_CR56","doi-asserted-by":"publisher","first-page":"3513","DOI":"10.62347\/RJBH6076","volume":"14","author":"YN Sung","year":"2024","unstructured":"Sung YN, Lee H, Kim E, Jung WY, Sohn JH, Lee YJ, et al. Interpretable deep learning model to predict lymph node metastasis in early gastric cancer using whole slide images. Am J Cancer Res. 2024;14(13):3513\u20133522.","journal-title":"Am J Cancer Res"},{"issue":"12","key":"5181_CR57","doi-asserted-by":"publisher","first-page":"6276","DOI":"10.3390\/ijms20246276","volume":"20","author":"M Joo","year":"2019","unstructured":"Joo M, Park A, Kim K, et al. A deep learning model for cell growth inhibition IC50 prediction and its application for gastric cancer patients. Int J Mol Sci. 2019;20(12):6276.","journal-title":"Int J Mol Sci"},{"key":"5181_CR58","doi-asserted-by":"publisher","first-page":"1329983","DOI":"10.3389\/fonc.2024.1329983","volume":"14","author":"J Zeng","year":"2024","unstructured":"Zeng J, Song D, Li K, Cao F, Zheng Y, et al. Deep learning model for predicting postoperative survival of patients with gastric cancer. Front Oncol. 2024;14:1329983.","journal-title":"Front Oncol"},{"key":"5181_CR59","doi-asserted-by":"publisher","first-page":"101864","DOI":"10.1016\/j.tranon.2023.101864","volume":"40","author":"Y Tan","year":"2024","unstructured":"Tan Y, Feng LJ, Huang YH, Xue JW, Long LL, Feng ZB, et al. A comprehensive radiopathological nomogram for prediction of pathological staging in gastric cancer using CT\u2013derived and WSI\u2013based features. Transl Oncol. 2024;40:101864.","journal-title":"Transl Oncol"},{"issue":"1","key":"5181_CR60","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1186\/s13020-025-01059-4","volume":"20","author":"S Yu","year":"2025","unstructured":"Yu S, Jiang H, Xia J, Gu J, Chen M, Wang Y, et al. Construction of machine learning\u2013based models for screening high\u2013risk patients with gastric precancerous lesions. Chin Med J. 2025;20(1):7.","journal-title":"Chin Med J"}],"container-title":["Abdominal Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00261-025-05181-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00261-025-05181-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00261-025-05181-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T13:03:57Z","timestamp":1774357437000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00261-025-05181-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,11]]},"references-count":60,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["5181"],"URL":"https:\/\/doi.org\/10.1007\/s00261-025-05181-7","relation":{},"ISSN":["2366-0058"],"issn-type":[{"value":"2366-0058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,11]]},"assertion":[{"value":"3 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 August 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 August 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 September 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}}]}}