{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T13:08:12Z","timestamp":1788872892017,"version":"build-2803163510"},"reference-count":107,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T00:00:00Z","timestamp":1788825600000},"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:p>Artificial intelligence (AI)-driven digital pathology has emerged as a promising approach for improving diagnosis, prognostic assessment, and treatment decision-making in hepatobiliary and pancreatic cancers (HPCs). By leveraging whole-slide imaging and deep learning, AI enables high-throughput and reproducible analysis of complex histopathological data, facilitating precision oncology. This review summarizes recent advances in AI-assisted digital pathology across hepatocellular carcinoma, biliary tract cancer, and pancreatic cancer, with a focus on diagnostic applications, molecular prediction, prognostic modeling, and treatment response assessment. Despite rapid technical progress, the clinical translation of AI remains limited. Current challenges extend beyond model performance and include data heterogeneity, limited interpretability, lack of prospective validation, and insufficient integration into clinical workflows. Notably, the central limitation lies in the misalignment between algorithm development and clinically relevant problem definition. Future research should prioritize multicenter validation, multimodal data integration, and the development of biologically informed and clinically interpretable models. Importantly, AI is expected to evolve from a diagnostic support tool to a dynamic, workflow-integrated decision-support system. Overall, the impact of AI-driven digital pathology will depend not only on technological advancement, but on its ability to deliver measurable clinical benefit and to be effectively embedded into real-world clinical practice.<\/jats:p>","DOI":"10.3389\/fonc.2026.1871770","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T12:24:27Z","timestamp":1788870267000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Insights into artificial intelligence-based digital pathology in hepatobiliary and pancreatic cancer"],"prefix":"10.3389","volume":"16","author":[{"given":"He-Yu","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, First Hospital of Jilin University","place":["Changchun, Jilin, China"]},{"name":"China-Singapore Belt and Road Joint Laboratory on Liver Disease Research, First Hospital of Jilin University","place":["Changchun, Jilin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alfred Wei Chieh","family":"Kow","sequence":"additional","affiliation":[{"name":"Division of Hepatobiliary & Pancreatic Surgery, Department of Surgery, National University Hospital Singapore","place":["Singapore, Singapore"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhong-Qi","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, First Hospital of Jilin University","place":["Changchun, Jilin, China"]},{"name":"China-Singapore Belt and Road Joint Laboratory on Liver Disease Research, First Hospital of Jilin University","place":["Changchun, Jilin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guo-Yue","family":"Lv","sequence":"additional","affiliation":[{"name":"Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, First Hospital of Jilin University","place":["Changchun, Jilin, China"]},{"name":"China-Singapore Belt and Road Joint Laboratory on Liver Disease Research, First Hospital of Jilin University","place":["Changchun, Jilin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,8]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(19)30154-8","article-title":"Digital pathology and artificial intelligence","volume":"20","author":"Niazi","year":"2019","journal-title":"Lancet Oncol"},{"key":"B2","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun ACM"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1136\/egastro-2023-100002","article-title":"Application of artificial intelligence in the diagnosis of hepatocellular carcinoma","volume":"1","author":"Koh","year":"2023","journal-title":"eGastroenterology"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1038\/s41575-024-00919-y","article-title":"Artificial intelligence in liver cancer - new tools for research and patient management","volume":"21","author":"Calderaro","year":"2024","journal-title":"Nat Rev Gastroenterol Hepatol"},{"key":"B5","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.jhep.2018.03.019","article-title":"EASL clinical practice guidelines: Management of hepatocellular carcinoma","volume":"69","year":"2018","journal-title":"J Hepatol"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1016\/j.esmoop.2023.101567","article-title":"The management of hepatocellular carcinoma. Current expert opinion and recommendations derived from the 24th ESMO\/World Congress on Gastrointestinal Cancer, Barcelona, 2022","volume":"8","author":"Ducreux","year":"2023","journal-title":"ESMO Open"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1038\/s43018-022-00436-4","article-title":"Artificial intelligence in histopathology: enhancing cancer research and clinical oncology","volume":"3","author":"Shmatko","year":"2022","journal-title":"Nat Cancer"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1053\/j.gastro.2022.02.025","article-title":"Deep learning-based classification of hepatocellular nodular lesions on whole-slide histopathologic images","volume":"162","author":"Cheng","year":"2022","journal-title":"Gastroenterology"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-0232-8","article-title":"Impact of a deep learning assistant on the histopathologic classification of liver cancer","volume":"3","author":"Kiani","year":"2020","journal-title":"NPJ Digit Med"},{"key":"B10","doi-asserted-by":"publisher","first-page":"8290","DOI":"10.1038\/s41467-023-43749-3","article-title":"Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma","volume":"14","author":"Calderaro","year":"2023","journal-title":"Nat Commun"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2022.857140","article-title":"Up-to-date pathologic classification and molecular characteristics of intrahepatic cholangiocarcinoma","volume":"9","author":"Chung","year":"2022","journal-title":"Front Med (Lausanne)"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1053\/j.gastro.2023.07.026","article-title":"Deep learning-enabled diagnosis of liver adenocarcinoma","volume":"165","author":"Albrecht","year":"2023","journal-title":"Gastroenterology"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.3390\/cancers15225389","article-title":"Deep learning for the pathologic diagnosis of hepatocellular carcinoma, cholangiocarcinoma, and metastatic colorectal cancer","volume":"15","author":"Jang","year":"2023","journal-title":"Cancers (Basel)"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03512-4","article-title":"AI-based pathology predicts origins for cancers of unknown primary","volume":"594","author":"Lu","year":"2021","journal-title":"Nature"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1136\/egastro-2025-100193","article-title":"Blood microbial DNA signature differentiates hepatocellular carcinoma from metastatic lesions","volume":"3","author":"Guccione","year":"2025","journal-title":"eGastroenterology"},{"key":"B16","doi-asserted-by":"crossref","DOI":"10.1109\/BIBM47256.2019.8983139","article-title":"Predicting tumor mutational burden from liver cancer pathological images using convolutional neural network","volume-title":"2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","author":"Zhang","year":"2019"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0177-5","article-title":"Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning","volume":"24","author":"Coudray","year":"2018","journal-title":"Nat Med"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhep.2019.06.001","article-title":"Molecular and histological correlations in liver cancer","volume":"71","author":"Calderaro","year":"2019","journal-title":"J Hepatol"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1002\/hep.29762","article-title":"Macrotrabecular-massive hepatocellular carcinoma: a distinctive histological subtype with clinical relevance","volume":"68","author":"Ziol","year":"2018","journal-title":"Hepatology"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1158\/1078-0432.CCR-14-0122","article-title":"Intratumor heterogeneity in hepatocellular carcinoma","volume":"21","author":"Friemel","year":"2015","journal-title":"Clin Cancer Res"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhep.2017.05.014","article-title":"Histological subtypes of hepatocellular carcinoma are related to gene mutations and molecular tumour classification","volume":"67","author":"Calderaro","year":"2017","journal-title":"J Hepatol"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.3748\/wjg.v21.i12.3472","article-title":"Lessons from rare tumors: hepatic lymphoepithelioma-like carcinomas","volume":"21","author":"Solinas","year":"2015","journal-title":"World J Gastroenterol"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1097\/PAS.0b013e3181f31caa","article-title":"Steatohepatitic hepatocellular carcinoma (SH-HCC): a distinctive histological variant of HCC in hepatitis C virus-related cirrhosis with associated NAFLD\/NASH","volume":"34","author":"Salomao","year":"2010","journal-title":"Am J Surg Pathol"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhep.2023.08.017","article-title":"Molecular-based targeted therapies in patients with hepatocellular carcinoma and hepato-cholangiocarcinoma refractory to atezolizumab\/bevacizumab","volume":"79","author":"Limousin","year":"2023","journal-title":"J Hepatol"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1136\/egastro-2025-100186","article-title":"Epigenetic mechanisms involved in hepatocellular carcinoma development and progression","volume":"3","author":"Bueloni","year":"2025","journal-title":"eGastroenterology"},{"key":"B26","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":"B27","doi-asserted-by":"publisher","DOI":"10.1038\/s43018-020-0085-8","article-title":"Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis","volume":"1","author":"Fu","year":"2020","journal-title":"Nat Cancer"},{"key":"B28","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":"B29","doi-asserted-by":"publisher","DOI":"10.1002\/ctm2.102","article-title":"Deep learning-based classification and mutation prediction from histopathological images of hepatocellular carcinoma","volume":"10","author":"Liao","year":"2020","journal-title":"Clin Transl Med"},{"key":"B30","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1038\/s41698-020-0120-3","article-title":"Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning","volume":"4","author":"Chen","year":"2020","journal-title":"NPJ Precis Oncol"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.adq0856","article-title":"Generative adversarial networks accurately reconstruct pan-cancer histology from pathologic, genomic, and radiographic latent features","volume":"10","author":"Howard","year":"2024","journal-title":"Sci Adv"},{"key":"B32","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1126\/science.aaf2403","article-title":"Visualization and analysis of gene expression in tissue sections by spatial transcriptomics","volume":"353","author":"Stahl","year":"2016","journal-title":"Science"},{"key":"B33","doi-asserted-by":"publisher","first-page":"2419","DOI":"10.1038\/s41467-018-04724-5","article-title":"Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity","volume":"9","author":"Berglund","year":"2018","journal-title":"Nat Commun"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.1101\/2021.07.12.452018","article-title":"The spatial landscape of clonal somatic mutations in benign and Malignant tissue","author":"Erickson","year":"2021","journal-title":"bioRxiv"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-021-00911-1","article-title":"A single-cell and spatially resolved atlas of human breast cancers","volume":"53","author":"Wu","year":"2021","journal-title":"Nat Genet"},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.1158\/0008-5472.CAN-18-0747","article-title":"Spatially resolved transcriptomics enables dissection of genetic heterogeneity in stage III cutaneous Malignant melanoma","volume":"78","author":"Thrane","year":"2018","journal-title":"Cancer Res"},{"key":"B37","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1038\/s41586-021-04217-4","article-title":"Spatial genomics enables multi-modal study of clonal heterogeneity in tissues","volume":"601","author":"Zhao","year":"2022","journal-title":"Nature"},{"key":"B38","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1038\/s41586-022-05425-2","article-title":"Spatial genomics maps the structure, nature and evolution of cancer clones","volume":"611","author":"Lomakin","year":"2022","journal-title":"Nature"},{"key":"B39","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-020-00682-w","article-title":"Data-efficient and weakly supervised computational pathology on whole-slide images","volume":"5","author":"Lu","year":"2021","journal-title":"Nat BioMed Eng"},{"key":"B40","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1038\/s41379-021-00919-2","article-title":"Digital pathology and artificial intelligence in translational medicine and clinical practice","volume":"35","author":"Baxi","year":"2022","journal-title":"Mod Pathol"},{"key":"B41","doi-asserted-by":"publisher","DOI":"10.3390\/cancers12123562","article-title":"Single-cell spatial analysis of tumor and immune microenvironment on whole-slide image reveals hepatocellular carcinoma subtypes","volume":"12","author":"Wang","year":"2020","journal-title":"Cancers (Basel)"},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.1097\/HEP.0000000000000600","article-title":"Spatial proximity of tumor-immune interactions predicts patient outcome in hepatocellular carcinoma","volume":"79","author":"Maestri","year":"2024","journal-title":"Hepatology"},{"key":"B43","doi-asserted-by":"publisher","DOI":"10.1038\/s41416-020-01122-x","article-title":"Deep learning in cancer pathology: a new generation of clinical biomarkers","volume":"124","author":"Echle","year":"2021","journal-title":"Br J Cancer"},{"key":"B44","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1186\/s12920-024-01796-9","article-title":"A systematic analysis of deep learning in genomics and histopathology for precision oncology","volume":"17","author":"Unger","year":"2024","journal-title":"BMC Med Genomics"},{"key":"B45","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1038\/s41568-020-00327-9","article-title":"Designing deep learning studies in cancer diagnostics","volume":"21","author":"Kleppe","year":"2021","journal-title":"Nat Rev Cancer"},{"key":"B46","doi-asserted-by":"publisher","DOI":"10.1002\/hep.31207","article-title":"Predicting survival after hepatocellular carcinoma resection using deep learning on histological slides","volume":"72","author":"Saillard","year":"2020","journal-title":"Hepatology"},{"key":"B47","doi-asserted-by":"publisher","DOI":"10.1136\/gutjnl-2020-320930","article-title":"Exploring prognostic indicators in the pathological images of hepatocellular carcinoma based on deep learning","volume":"70","author":"Shi","year":"2021","journal-title":"Gut"},{"key":"B48","doi-asserted-by":"publisher","DOI":"10.21203\/rs.3.rs-1452983\/v1","article-title":"Deep learning supported discovery of biomarkers for clinical prognosis of liver cancer","volume":"5","author":"Kong","year":"2023","journal-title":"Nat Mach Intell"},{"key":"B49","doi-asserted-by":"publisher","DOI":"10.1038\/s41568-021-00408-3","article-title":"Harnessing multimodal data integration to advance precision oncology","volume":"22","author":"Boehm","year":"2022","journal-title":"Nat Rev Cancer"},{"key":"B50","doi-asserted-by":"publisher","DOI":"10.1016\/j.ccell.2022.07.004","article-title":"Pan-cancer integrative histology-genomic analysis via multimodal deep learning","volume":"40","author":"Chen","year":"2022","journal-title":"Cancer Cell"},{"key":"B51","doi-asserted-by":"publisher","first-page":"2047","DOI":"10.1038\/s41598-021-81506-y","article-title":"Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images","volume":"11","author":"Yamashita","year":"2021","journal-title":"Sci Rep"},{"key":"B52","doi-asserted-by":"publisher","DOI":"10.1111\/his.15001","article-title":"Artificial intelligence-based reticulin proportionate area - a novel histological outcome predictor in hepatocellular carcinoma","volume":"83","author":"Patil","year":"2023","journal-title":"Histopathology"},{"key":"B53","doi-asserted-by":"publisher","DOI":"10.1136\/gutjnl-2025-337945","article-title":"Token-guided multimodal prognosis in hepatocellular carcinoma: a framework steered by tumour-stroma ratio","author":"Huang","year":"2026","journal-title":"Gut"},{"key":"B54","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":"B55","doi-asserted-by":"publisher","DOI":"10.1016\/j.esmoop.2022.100400","article-title":"Artificial intelligence for detection of microsatellite instability in colorectal cancer-a multicentric analysis of a pre-screening tool for clinical application","volume":"7","author":"Echle","year":"2022","journal-title":"ESMO Open"},{"key":"B56","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(20)30535-0","article-title":"Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study","volume":"22","author":"Yamashita","year":"2021","journal-title":"Lancet Oncol"},{"key":"B57","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13030391","article-title":"Artificial intelligence for histology-based detection of microsatellite instability and prediction of response to immunotherapy in colorectal cancer","volume":"13","author":"Hildebrand","year":"2021","journal-title":"Cancers (Basel)"},{"key":"B58","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(21)00180-1","article-title":"Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study","volume":"3","author":"Bilal","year":"2021","journal-title":"Lancet Digit Health"},{"key":"B59","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":"B60","doi-asserted-by":"publisher","DOI":"10.1016\/j.xcrm.2021.100400","article-title":"Predicting endometrial cancer subtypes and molecular features from histopathology images using multi-resolution deep learning models","volume":"2","author":"Hong","year":"2021","journal-title":"Cell Rep Med"},{"key":"B61","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1500596","article-title":"PD-1 blockade in tumors with mismatch-repair deficiency","volume":"372","author":"Le","year":"2015","journal-title":"N Engl J Med"},{"key":"B62","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhep.2022.01.018","article-title":"Artificial intelligence predicts immune and inflammatory gene signatures directly from hepatocellular carcinoma histology","volume":"77","author":"Zeng","year":"2022","journal-title":"J Hepatol"},{"key":"B63","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(23)00468-0","article-title":"Artificial intelligence-based pathology as a biomarker of sensitivity to atezolizumab-bevacizumab in patients with hepatocellular carcinoma: a multicentre retrospective study","volume":"24","author":"Zeng","year":"2023","journal-title":"Lancet Oncol"},{"key":"B64","doi-asserted-by":"publisher","DOI":"10.1093\/bjrai\/ubaf018","article-title":"CT-based deep learning prediction of complete response in intermediate-stage hepatocellular carcinoma treated with drug-eluting beads transarterial chemoembolization","volume":"2","author":"Dana","year":"2025","journal-title":"BJR Artif Intell"},{"key":"B65","doi-asserted-by":"publisher","DOI":"10.1016\/j.annonc.2022.10.506","article-title":"Biliary tract cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up","volume":"34","author":"Vogel","year":"2023","journal-title":"Ann Oncol"},{"key":"B66","doi-asserted-by":"publisher","DOI":"10.1038\/s41575-020-0310-z","article-title":"Cholangiocarcinoma 2020: the next horizon in mechanisms and management","volume":"17","author":"Banales","year":"2020","journal-title":"Nat Rev Gastroenterol Hepatol"},{"key":"B67","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.ymeth.2021.04.005","article-title":"Diagnosis of cholangiocarcinoma from microscopic hyperspectral pathological dataset by deep convolution neural networks","volume":"202","author":"Sun","year":"2022","journal-title":"Methods"},{"key":"B68","doi-asserted-by":"publisher","first-page":"18854","DOI":"10.1038\/s41598-023-46152-6","article-title":"Lightweight neural network for smart diagnosis of cholangiocarcinoma using histopathological images","volume":"13","author":"Chakrabarti","year":"2023","journal-title":"Sci Rep"},{"key":"B69","doi-asserted-by":"publisher","DOI":"10.1111\/hepr.13867","article-title":"Automated image analysis of keratin 7 staining can predict disease outcome in primary sclerosing cholangitis","volume":"53","author":"Sjoblom","year":"2023","journal-title":"Hepatol Res"},{"key":"B70","doi-asserted-by":"publisher","DOI":"10.1007\/s12664-024-01518-0","article-title":"Applications of artificial intelligence in biliary tract cancers","volume":"43","author":"Gupta","year":"2024","journal-title":"Indian J Gastroenterol"},{"key":"B71","doi-asserted-by":"publisher","DOI":"10.1016\/j.critrevonc.2023.104235","article-title":"Immunotherapy and targeted therapy for cholangiocarcinoma: Artificial intelligence research in imaging","volume":"194","author":"Liu","year":"2024","journal-title":"Crit Rev Oncol Hematol"},{"key":"B72","doi-asserted-by":"publisher","DOI":"10.3390\/ijms25116002","article-title":"The diagnostic and prognostic potentials of non-coding RNA in cholangiocarcinoma","volume":"25","author":"Andrade","year":"2024","journal-title":"Int J Mol Sci"},{"key":"B73","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-02857-3","article-title":"Towards a general-purpose foundation model for computational pathology","volume":"30","author":"Chen","year":"2024","journal-title":"Nat Med"},{"key":"B74","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-02856-4","article-title":"A visual-language foundation model for computational pathology","volume":"30","author":"Lu","year":"2024","journal-title":"Nat Med"},{"key":"B75","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105520","article-title":"Survival prediction on intrahepatic cholangiocarcinoma with histomorphological analysis on the whole slide images","volume":"146","author":"Xie","year":"2022","journal-title":"Comput Biol Med"},{"key":"B76","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhep.2021.10.030","article-title":"Distribution and density of tertiary lymphoid structures predict clinical outcome in intrahepatic cholangiocarcinoma","volume":"76","author":"Ding","year":"2022","journal-title":"J Hepatol"},{"key":"B77","doi-asserted-by":"publisher","first-page":"190305257","DOI":"10.48550\/arXiv.1903.05257","article-title":"Towards unsupervised cancer subtyping: Predicting prognosis using a histologic visual dictionary","author":"Muhammad","year":"2019","journal-title":"arXiv preprint arXiv"},{"key":"B78","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1186\/s12916-024-03482-0","article-title":"Mining the interpretable prognostic features from pathological image of intrahepatic cholangiocarcinoma using multi-modal deep learning","volume":"22","author":"Ding","year":"2024","journal-title":"BMC Med"},{"key":"B79","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpi.2023.100345","article-title":"AI-based digital histopathology for perihilar cholangiocarcinoma: A step, not a jump","volume":"15","author":"Hoyer","year":"2024","journal-title":"J Pathol Inform"},{"key":"B80","doi-asserted-by":"publisher","DOI":"10.1007\/s12072-023-10487-z","article-title":"Artificial intelligence CT radiomics to predict early recurrence of intrahepatic cholangiocarcinoma: a multicenter study","volume":"17","author":"Song","year":"2023","journal-title":"Hepatol Int"},{"key":"B81","doi-asserted-by":"publisher","DOI":"10.1056\/EVIDoa2200015","article-title":"Durvalumab plus gemcitabine and cisplatin in advanced biliary tract cancer","volume":"1","author":"Oh","year":"2022","journal-title":"NEJM Evid"},{"key":"B82","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.jhep.2023.03.010","article-title":"EASL-ILCA Clinical Practice Guidelines on the management of intrahepatic cholangiocarcinoma","volume":"79","year":"2023","journal-title":"J Hepatol"},{"key":"B83","doi-asserted-by":"publisher","DOI":"10.1136\/gutjnl-2022-327099","article-title":"Clinical relevance of biomarkers in cholangiocarcinoma: critical revision and future directions","volume":"71","author":"Macias","year":"2022","journal-title":"Gut"},{"key":"B84","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/175271","article-title":"New morphological features for grading pancreatic ductal adenocarcinomas","volume":"2013","author":"Song","year":"2013","journal-title":"BioMed Res Int"},{"key":"B85","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21660","article-title":"Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J Clin"},{"key":"B86","doi-asserted-by":"publisher","DOI":"10.1053\/j.gastro.2023.04.003","article-title":"Artificial intelligence in pancreatic ductal adenocarcinoma imaging: A commentary on potential future applications","volume":"165","author":"Schuurmans","year":"2023","journal-title":"Gastroenterology"},{"key":"B87","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14215382","article-title":"Artificial intelligence-driven diagnosis of pancreatic cancer","volume":"14","author":"Hameed","year":"2022","journal-title":"Cancers (Basel)"},{"key":"B88","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compbiomed.2012.10.009","article-title":"Automatic differential diagnosis of pancreatic serous and mucinous cystadenomas based on morphological features","volume":"43","author":"Song","year":"2013","journal-title":"Comput Biol Med"},{"key":"B89","doi-asserted-by":"publisher","first-page":"6893","DOI":"10.1038\/s41598-019-43314-3","article-title":"Diagnostic ability of artificial intelligence using deep learning analysis of cyst fluid in differentiating Malignant from benign pancreatic cystic lesions","volume":"9","author":"Kurita","year":"2019","journal-title":"Sci Rep"},{"key":"B90","doi-asserted-by":"publisher","DOI":"10.3748\/wjg.v29.i12.1811","article-title":"Artificial intelligence as a noninvasive tool for pancreatic cancer prediction and diagnosis","volume":"29","author":"Faur","year":"2023","journal-title":"World J Gastroenterol"},{"key":"B91","doi-asserted-by":"publisher","DOI":"10.1002\/cac2.12204","article-title":"Early screening and diagnosis strategies of pancreatic cancer: a comprehensive review","volume":"41","author":"Yang","year":"2021","journal-title":"Cancer Commun (Lond)"},{"key":"B92","doi-asserted-by":"publisher","DOI":"10.1159\/000510755","article-title":"Fine needle aspiration versus fine needle biopsy of biliopancreatic lesions: Are they really opposing techniques or can they be complementary? Our experience in a large cohort of cases from a single institution","volume":"65","author":"Nicola","year":"2021","journal-title":"Acta Cytol"},{"key":"B93","doi-asserted-by":"publisher","DOI":"10.1002\/cncy.21915","article-title":"Computer-assisted cytologic diagnosis in pancreatic FNA: An application of neural networks to image analysis","volume":"125","author":"Momeni-Boroujeni","year":"2017","journal-title":"Cancer Cytopathol"},{"key":"B94","doi-asserted-by":"publisher","DOI":"10.3390\/ijms22105385","article-title":"Deep learning in pancreatic tissue: Identification of anatomical structures, pancreatic intraepithelial neoplasia, and ductal adenocarcinoma","volume":"22","author":"Kriegsmann","year":"2021","journal-title":"Int J Mol Sci"},{"key":"B95","doi-asserted-by":"publisher","DOI":"10.3233\/CBM-210308","article-title":"Machine learning analyses of highly-multiplexed immunofluorescence identifies distinct tumor and stromal cell populations in primary pancreatic tumors","volume":"33","author":"Vance","year":"2022","journal-title":"Cancer biomark"},{"key":"B96","doi-asserted-by":"publisher","DOI":"10.1136\/egastro-2024-100082","article-title":"Pancreatic incidentaloma: incidental findings from history towards the era of liquid biopsy","volume":"2","author":"L\u00f6hr","year":"2024","journal-title":"eGastroenterology"},{"key":"B97","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.665929","article-title":"Automatic pancreatic ductal adenocarcinoma detection in whole slide images using deep convolutional neural networks","volume":"11","author":"Fu","year":"2021","journal-title":"Front Oncol"},{"key":"B98","doi-asserted-by":"publisher","DOI":"10.1038\/s41568-021-00399-1","article-title":"Artificial intelligence in cancer research, diagnosis and therapy","volume":"21","author":"Elemento","year":"2021","journal-title":"Nat Rev Cancer"},{"key":"B99","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0195621","article-title":"Identifying tumor in pancreatic neuroendocrine neoplasms from Ki67 images using transfer learning","volume":"13","author":"Niazi","year":"2018","journal-title":"PloS One"},{"key":"B100","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2025.1727806","article-title":"The place of advanced machine learning techniques in building pancreatic adenocarcinoma survival and recurrence prognosis models","volume":"15","author":"Avram","year":"2025","journal-title":"Front Oncol"},{"key":"B101","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-03141-0","article-title":"A foundation model for clinical-grade computational pathology and rare cancers detection","volume":"30","author":"Vorontsov","year":"2024","journal-title":"Nat Med"},{"key":"B102","doi-asserted-by":"publisher","DOI":"10.1016\/j.trecan.2024.08.008","article-title":"Scaling data toward pan-cancer foundation models","volume":"10","author":"Khalili","year":"2024","journal-title":"Trends Cancer"},{"key":"B103","doi-asserted-by":"publisher","DOI":"10.1007\/s00292-024-01299-5","article-title":"Die modelltransferierbarkeit von KI in der digitalen Pathologie","volume":"45","author":"Mayer","year":"2024","journal-title":"Die Pathologie"},{"key":"B104","doi-asserted-by":"publisher","DOI":"10.1136\/bmjgast-2025-001832","article-title":"Enhancing ultrasonographic detection of hepatocellular carcinoma with artificial intelligence: current applications, challenges and future directions","volume":"12","author":"Wongsuwan","year":"2025","journal-title":"BMJ Open Gastroenterol"},{"key":"B105","doi-asserted-by":"publisher","DOI":"10.1136\/bmjgast-2025-001896","article-title":"Large language models for extracting histopathologic diagnoses of colorectal cancer and dysplasia from electronic health records","volume":"12","author":"Johnson","year":"2025","journal-title":"BMJ Open Gastroenterol"},{"key":"B106","doi-asserted-by":"publisher","DOI":"10.5858\/arpa.2022-0066-OA","article-title":"Clinical validation of artificial intelligence-augmented pathology diagnosis demonstrates significant gains in diagnostic accuracy in prostate cancer detection","volume":"147","author":"Raciti","year":"2023","journal-title":"Arch Pathol Lab Med"},{"key":"B107","year":"","journal-title":"Artificial intelligence-enabled medical devices"}],"container-title":["Frontiers in Oncology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2026.1871770\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T12:24:29Z","timestamp":1788870269000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2026.1871770\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,8]]},"references-count":107,"alternative-id":["10.3389\/fonc.2026.1871770"],"URL":"https:\/\/doi.org\/10.3389\/fonc.2026.1871770","relation":{},"ISSN":["2234-943X"],"issn-type":[{"value":"2234-943X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,8]]},"article-number":"1871770"}}