{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T04:56:23Z","timestamp":1781585783950,"version":"3.54.5"},"reference-count":67,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T00:00:00Z","timestamp":1781568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>\n                      Gastric cancer (GC) exhibits substantial molecular heterogeneity, necessitating precision oncology approaches. Conventional biomarker assessments, including immunohistochemistry,\n                      <jats:italic>in situ<\/jats:italic>\n                      hybridization, polymerase chain reaction, and next-generation sequencing, are the diagnostic standard but resource-intensive and may be limited by tissue availability, cost, and turnaround time. Artificial intelligence (AI)\u2013enabled computational pathology using whole-slide images (WSIs) has emerged as a scalable approach for inferring molecular and immune phenotypes directly from routine hematoxylin and eosin\u2013stained histopathological images.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This review synthesizes recent advances in AI-based WSI analysis for biomarker assessment in GC, focusing on molecular subtype prediction, actionable genetic alterations, immune-related features, tumor microenvironment characterization, and multimodal integration.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>AI models have demonstrated promising performance in predicting microsatellite instability and Epstein\u2013Barr virus status, supporting their potential use as prescreening or triage tools to prioritize confirmatory testing. In addition, these approaches enable the quantitative characterization of the tumor microenvironment by mapping tertiary lymphoid structures and immune architecture, providing prognostic insights. Multimodal integration of histopathology with radiologic, genomic, and clinical data has shown improved predictive performance compared to single-modality approaches, particularly for recurrence and treatment responses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>However, challenges remain, including model interpretability, variability in performance across different datasets, and incomplete data across modalities. Future directions include prospective multicenter validation in clinical workflows, standardization of evaluation frameworks, and implementation of uncertainty estimation to support clinical decision-making. Overall, AI\u2013enabled digital pathology represents a promising approach for advancing precision oncology in GC by improving biomarker assessment and providing insights into tumor biology.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1845038","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T04:26:49Z","timestamp":1781584009000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence for biomarker prediction in gastric cancer: from histopathology to multimodal integration"],"prefix":"10.3389","volume":"16","author":[{"given":"Yesul","family":"Jeong","sequence":"first","affiliation":[{"name":"Department of Hospital Pathology, St. Vincent\u2019s Hospital, College of Medicine, The Catholic University of Korea","place":["Seoul, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sangjeong","family":"Ahn","sequence":"additional","affiliation":[{"name":"Department of Pathology, College of Medicine, Korea University","place":["Seoul, Republic of Korea"]},{"name":"Department of Biomedical Informatics, College of Medicine, Korea University","place":["Seoul, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sung Hak","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Hospital Pathology, Seoul St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea","place":["Seoul, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,16]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21834","article-title":"Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA Cancer J Clin"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1038\/nature13480","article-title":"Comprehensive molecular characterization of gastric adenocarcinoma","volume":"513","year":"2014","journal-title":"Nature"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1038\/s41571-021-00492-2","article-title":"Biomarker-targeted therapies for advanced-stage gastric and gastro-oesophageal junction cancers: an emerging paradigm","volume":"18","author":"Nakamura","year":"2021","journal-title":"Nat Rev Clin Oncol"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1016\/j.path.2023.05.004","article-title":"What therapeutic biomarkers in gastro-esophageal junction and gastric cancer should a pathologist know about","volume":"16","author":"Kumarasinghe","year":"2023","journal-title":"Surg Pathol Clin"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1177\/10668969231185089","article-title":"Whole slide imaging technology and its applications: current and emerging perspectives","volume":"32","author":"Jain","year":"2024","journal-title":"Int J Surg Pathol"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1016\/j.labinv.2024.102111","article-title":"Implementation of digital pathology and artificial intelligence in routine pathology practice","volume":"104","author":"Zhang","year":"2024","journal-title":"Lab Invest"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1038\/s43018-020-0087-6","article-title":"Pan-cancer image-based detection of clinically actionable genetic alterations","volume":"1","author":"Kather","year":"2020","journal-title":"Nat Cancer"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1038\/s41571-019-0252-y","article-title":"Artificial intelligence in digital pathology\u2014new tools for diagnosis and precision oncology","volume":"16","author":"Bera","year":"2019","journal-title":"Nat Rev Clin Oncol"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1002\/path.5898","article-title":"Artificial intelligence to identify genetic alterations in conventional histopathology","volume":"257","author":"Cifci","year":"2022","journal-title":"J Pathol"},{"key":"B10","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1186\/s12935-025-03756-4","article-title":"The artificial intelligence revolution in gastric cancer management: clinical applications","volume":"25","author":"Li","year":"2025","journal-title":"Cancer Cell Int"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1002\/advs.202508190","article-title":"Interpretable multimodal fusion model enhances postoperative recurrence prediction in gastric cancer","volume":"12","author":"Ding","year":"2025","journal-title":"Adv Sci (Weinh)"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-019-0462-y","article-title":"Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer","volume":"25","author":"Kather","year":"2019","journal-title":"Nat Med"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.3390\/cancers12123687","article-title":"Deep learning predicts underlying features on pathology images with therapeutic relevance for breast and gastric cancer","volume":"12","author":"Valieris","year":"2020","journal-title":"Cancers (Basel)"},{"key":"B14","doi-asserted-by":"publisher","first-page":"22636","DOI":"10.1038\/s41598-021-02168-4","article-title":"Detecting immunotherapy-sensitive subtype in gastric cancer using histologic image-based deep learning","volume":"11","author":"Hinata","year":"2021","journal-title":"Sci Rep"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2021.103631","article-title":"Accurate diagnosis and prognosis prediction of gastric cancer using deep learning on digital pathological images: a retrospective multicentre study","volume":"73","author":"Huang","year":"2021","journal-title":"EBiomedicine"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.3748\/wjg.v27.i44.7687","article-title":"Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach","volume":"27","author":"Jang","year":"2021","journal-title":"World J Gastroenterol"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(21)00133-3","article-title":"Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study","volume":"3","author":"Muti","year":"2021","journal-title":"Lancet Digit Health"},{"key":"B18","doi-asserted-by":"publisher","first-page":"1637","DOI":"10.1038\/s41467-021-21674-7","article-title":"Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning","volume":"12","author":"Wang","year":"2021","journal-title":"Nat Commun"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13236002","article-title":"Deep learning predicts EBV status in gastric cancer based on spatial patterns of lymphocyte infiltration","volume":"13","author":"Zhang","year":"2021","journal-title":"Cancers (Basel)"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1038\/s41379-022-01073-z","article-title":"Assessment of deep learning assistance for the pathological diagnosis of gastric cancer","volume":"35","author":"Ba","year":"2022","journal-title":"Mod Pathol"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1002\/path.5879","article-title":"Deep learning based on hematoxylin-eosin staining outperforms immunohistochemistry in predicting molecular subtypes of gastric adenocarcinoma","volume":"257","author":"Flinner","year":"2022","journal-title":"J Pathol"},{"key":"B22","doi-asserted-by":"publisher","first-page":"18466","DOI":"10.1038\/s41598-022-22731-x","article-title":"Deep learning model to predict Epstein-Barr virus associated gastric cancer in histology","volume":"12","author":"Jeong","year":"2022","journal-title":"Sci Rep"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102474","article-title":"Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology","volume":"79","author":"Ghaffari Laleh","year":"2022","journal-title":"Med Image Anal"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.1016\/j.apsb.2022.03.013","article-title":"An integrative approach of digital image analysis and transcriptome profiling to explore potential predictive biomarkers for TGFbeta blockade therapy","volume":"12","author":"Pomponio","year":"2022","journal-title":"Acta Pharm Sin B"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1038\/s41374-022-00742-6","article-title":"Interpretable tumor differentiation grade and microsatellite instability recognition in gastric cancer using deep learning","volume":"102","author":"Su","year":"2022","journal-title":"Lab Invest"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btac456","article-title":"DEMoS: a deep learning-based ensemble approach for predicting the molecular subtypes of gastric adenocarcinomas from histopathological images","volume":"38","author":"Wang","year":"2022","journal-title":"Bioinformatics"},{"key":"B27","doi-asserted-by":"publisher","first-page":"2790","DOI":"10.1038\/s41467-022-30459-5","article-title":"A deep learning model and human\u2013machine fusion for prediction of EBV-associated gastric cancer from histopathology","volume":"13","author":"Zheng","year":"2022","journal-title":"Nat Commun"},{"key":"B28","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1002\/ijc.34251","article-title":"Deep learning captures selective features for discrimination of microsatellite instability from pathologic tissue slides of gastric cancer","volume":"152","author":"Lee","year":"2023","journal-title":"Int J Cancer"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2022.52553","article-title":"Development and validation of a machine learning model for detection and classification of tertiary lymphoid structures in gastrointestinal cancers","volume":"6","author":"Li","year":"2023","journal-title":"JAMA Netw Open"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.1007\/s10120-022-01347-0","article-title":"Direct prediction of genetic aberrations from pathology images in gastric cancer with swarm learning","volume":"26","author":"Saldanha","year":"2023","journal-title":"Gastric Cancer"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1007\/s10120-023-01398-x","article-title":"Deep learning-based subtyping of gastric cancer histology predicts clinical outcome: a multi-institutional retrospective study","volume":"26","author":"Veldhuizen","year":"2023","journal-title":"Gastric Cancer"},{"key":"B32","doi-asserted-by":"publisher","first-page":"690","DOI":"10.1111\/cas.15592","article-title":"Survival prediction of stomach cancer using expression data and deep learning models with histopathological images","volume":"114","author":"Wei","year":"2023","journal-title":"Cancer Sci"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3286309","article-title":"Prediction of microsatellite instability from gastric histological images based on residual attention networks with non-local modules","volume":"11","author":"Yu","year":"2023","journal-title":"IEEE Access"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.1016\/j.mcp.2024.101983","article-title":"Prognostic prediction of gastric cancer based on H&E findings and machine learning pathomics","volume":"78","author":"Han","year":"2024","journal-title":"Mol Cell Probes"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.56740","article-title":"Development of an artificial intelligence model for the classification of gastric carcinoma stages using pathology slides","volume":"16","author":"Reddy","year":"2024","journal-title":"Cureus"},{"key":"B36","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1186\/s12967-023-04838-5","article-title":"Deeprisk network: an AI-based tool for digital pathology signature and treatment responsiveness of gastric cancer using whole-slide images","volume":"22","author":"Tian","year":"2024","journal-title":"J Transl Med"},{"key":"B37","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2025.110938","article-title":"Tumor microenvironment-based classification for predicting gastric cancer prognosis","volume":"197","author":"Hong","year":"2025","journal-title":"Comput Biol Med"},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.1002\/advs.202408451","article-title":"Artificial intelligence for predicting HER2 status of gastric cancer based on whole-slide histopathology images: A retrospective multicenter study","volume":"12","author":"Liao","year":"2025","journal-title":"Adv Sci (Weinh)"},{"key":"B39","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1038\/s41746-025-01580-8","article-title":"Deep Gaussian process with uncertainty estimation for microsatellite instability and immunotherapy response prediction from histology","volume":"8","author":"Park","year":"2025","journal-title":"NPJ Digit Med"},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.1158\/0008-5472.CAN-25-1793","article-title":"Artificial intelligence and multimodality data integration decipher tertiary lymphoid structure maturity in gastric cancer","volume":"85","author":"Wu","year":"2025","journal-title":"Cancer Res"},{"key":"B41","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105336","article-title":"Prediction of lymph node metastasis in primary gastric cancer from pathological images and clinical data by multimodal multiscale deep learning","volume":"86","author":"Guo","year":"2023","journal-title":"BioMed Signal Proc Control"},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.1016\/j.xcrm.2024.101848","article-title":"Interpretable multi-modal artificial intelligence model for predicting gastric cancer response to neoadjuvant chemotherapy","volume":"5","author":"Gao","year":"2024","journal-title":"Cell Rep Med"},{"key":"B43","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1038\/s41392-024-01932-y","article-title":"Predicting gastric cancer response to anti-HER2 therapy or anti-HER2 combined immunotherapy based on multi-modal data","volume":"9","author":"Chen","year":"2024","journal-title":"Signal Transduct Target Ther"},{"key":"B44","doi-asserted-by":"publisher","DOI":"10.1093\/bfgp\/elad032","article-title":"Predicting gastric cancer tumor mutational burden from histopathological images using multimodal deep learning","volume":"23","author":"Li","year":"2024","journal-title":"Brief Funct Genomics"},{"key":"B45","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2024.102871","article-title":"Improving diagnosis and outcome prediction of gastric cancer via multimodal learning using whole slide pathological images and gene expression","volume":"152","author":"Xie","year":"2024","journal-title":"Artif Intell Med"},{"key":"B46","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2025.108971","article-title":"Enhancing gastric cancer prognosis prediction via multi-step multi-modality ensemble survival modeling of HE-stained images and mIHC data","volume":"270","author":"Gao","year":"2025","journal-title":"Comput Methods Prog BioMed"},{"key":"B47","doi-asserted-by":"publisher","DOI":"10.1016\/j.canlet.2025.217930","article-title":"Multimodal radiopathomics signature for prediction of response to immunotherapy-based combination therapy in gastric cancer using interpretable machine learning","volume":"631","author":"Huang","year":"2025","journal-title":"Cancer Lett"},{"key":"B48","doi-asserted-by":"publisher","DOI":"10.1364\/BOE.541570","article-title":"MulitDeepsurv: survival analysis of gastric cancer based on deep learning multimodal fusion models","volume":"16","author":"Mao","year":"2025","journal-title":"BioMed Opt Express"},{"key":"B49","doi-asserted-by":"publisher","first-page":"20486","DOI":"10.1038\/s41598-021-99940-3","article-title":"A deep learning model for gastric diffuse-type adenocarcinoma classification in whole slide images","volume":"11","author":"Kanavati","year":"2021","journal-title":"Sci Rep"},{"key":"B50","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-023-01499-4","article-title":"Mismatch repair deficiency is not sufficient to elicit tumor immunogenicity","volume":"55","author":"Westcott","year":"2023","journal-title":"Nat Genet"},{"key":"B51","doi-asserted-by":"publisher","DOI":"10.1136\/gutjnl-2013-304531","article-title":"Improved survival of gastric cancer with tumour Epstein-Barr virus positivity: an international pooled analysis","volume":"63","author":"Camargo","year":"2014","journal-title":"Gut"},{"key":"B52","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(10)61121-X","article-title":"Trastuzumab in combination with chemotherapy versus chemotherapy alone for treatment of HER2-positive advanced gastric or gastro-oesophageal junction cancer (ToGA): a phase 3, open-label, randomised controlled trial","volume":"376","author":"Bang","year":"2010","journal-title":"Lancet"},{"key":"B53","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa2503119","article-title":"Trastuzumab deruxtecan or ramucirumab plus paclitaxel in gastric cancer","volume":"393","author":"Shitara","year":"2025","journal-title":"N Engl J Med"},{"key":"B54","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13153811","article-title":"Deep learning for automatic subclassification of gastric carcinoma using whole-slide histopathology images","volume":"13","author":"Jang","year":"2021","journal-title":"Cancers (Basel)"},{"key":"B55","doi-asserted-by":"publisher","DOI":"10.1158\/1078-0432.CCR-21-0327","article-title":"FDA approval summary: pembrolizumab for the treatment of tumor mutational burden-high solid tumors","volume":"27","author":"Marcus","year":"2021","journal-title":"Clin Cancer Res"},{"key":"B56","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaf580","article-title":"Deep learning-based fusion of nuclear segmentation features for microsatellite instability and tumor mutational burden prediction in digestive tract cancers: a multicenter validation study","volume":"26","author":"Zhang","year":"2025","journal-title":"Brief Bioinform"},{"key":"B57","doi-asserted-by":"publisher","DOI":"10.4132\/jptm.2024.03.15","article-title":"Interpretation of PD-L1 expression in gastric cancer: summary of a consensus meeting of Korean gastrointestinal pathologists","volume":"58","author":"Ahn","year":"2024","journal-title":"J Pathol Transl Med"},{"key":"B58","doi-asserted-by":"publisher","DOI":"10.5230\/jgc.2025.25.e4","article-title":"PD-L1 as a biomarker in gastric cancer immunotherapy","volume":"25","author":"Cho","year":"2025","journal-title":"J Gastric Cancer"},{"key":"B59","doi-asserted-by":"publisher","DOI":"10.4103\/jpi.jpi_24_19","article-title":"Multi-field-of-view deep learning model predicts nonsmall cell lung cancer programmed death-ligand 1 status from whole-slide hematoxylin and eosin images","volume":"10","author":"Sha","year":"2019","journal-title":"J Pathol Inform"},{"key":"B60","doi-asserted-by":"publisher","first-page":"6753","DOI":"10.1038\/s41467-022-34275-9","article-title":"Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer","volume":"13","author":"Shamai","year":"2022","journal-title":"Nat Commun"},{"key":"B61","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2025.1614099","article-title":"Automated interpretation of PD-L1 CPS based on multi-AI models integration strategy in gastric cancer","volume":"16","author":"Han","year":"2025","journal-title":"Front Immunol"},{"key":"B62","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1146\/annurev-bioeng-112415-114722","article-title":"Emerging themes in image informatics and molecular analysis for digital pathology","volume":"18","author":"Bhargava","year":"2016","journal-title":"Annu Rev BioMed Eng"},{"key":"B63","doi-asserted-by":"publisher","DOI":"10.1136\/jitc-2024-011346","article-title":"Artificial intelligence-based digital pathology using H&E-stained whole slide images in immuno-oncology: from immune biomarker detection to immunotherapy response prediction","volume":"13","author":"Zhang","year":"2025","journal-title":"J Immunother Cancer"},{"key":"B64","doi-asserted-by":"publisher","DOI":"10.1007\/s00261-025-05181-7","article-title":"Artificial intelligence in gastric cancer: a systematic review of machine learning and deep learning applications","volume":"51","author":"Alsallal","year":"2026","journal-title":"Abdom Radiol (NY)"},{"key":"B65","volume-title":"Building an AI-Ready America: Adopting AI at Work","year":"2026"},{"key":"B66","article-title":"Proposal for a Regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act)","year":"2021"},{"key":"B67","volume-title":"Guidance on the Review and Approval of Artificial Intelligence (AI)-based Medical Devices","year":"2022"}],"container-title":["Frontiers in Oncology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2026.1845038\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T04:26:54Z","timestamp":1781584014000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2026.1845038\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,16]]},"references-count":67,"alternative-id":["10.3389\/fonc.2026.1845038"],"URL":"https:\/\/doi.org\/10.3389\/fonc.2026.1845038","relation":{},"ISSN":["2234-943X"],"issn-type":[{"value":"2234-943X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,16]]},"article-number":"1845038"}}