{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T12:29:03Z","timestamp":1786969743783,"version":"build-2736575974"},"reference-count":67,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T00:00:00Z","timestamp":1786924800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korean government","award":["RS2024-00441029"],"award-info":[{"award-number":["RS2024-00441029"]}]},{"name":"Ministry of Health & Welfare, Republic of Korea","award":["HR22C1302"],"award-info":[{"award-number":["HR22C1302"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Mol. Sci.","IJMS"],"abstract":"<jats:p>Endometrial cancer is one of the most rapidly increasing gynaecological malignancies worldwide. The clinically adapted molecular classification of endometrial carcinoma, derived from The Cancer Genome Atlas, comprises four major subtypes: POLE-mutated, mismatch repair-deficient, p53-abnormal expression, and no specific molecular profile. Its clinical implementation has improved prognostic stratification, risk assessment, and treatment decision-making in patients with endometrial carcinoma. However, current workflows rely on immunohistochemistry and targeted sequencing, which increase costs, turnaround times, and infrastructure requirements, thereby limiting their universal adoption in routine clinical practice. Recent advances in artificial intelligence (AI), particularly deep learning models capable of predicting molecular features directly from H&amp;E-stained whole-slide images, have emerged as promising tools for precision oncology. In addition to reproducing established molecular classification, these approaches may reveal previously unrecognised biomarker-defined histologic patterns that are difficult to detect using conventional methods. This article synthesises the current evidence on AI-based molecular classification in endometrial carcinoma from a pathologist-centred perspective, emphasising the biological rationale, methodological limitations, and future directions for clinical translation.<\/jats:p>","DOI":"10.3390\/ijms27167341","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T12:01:26Z","timestamp":1786968086000},"page":"7341","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence and Digital Pathology for Molecular Classification of Endometrial Cancer"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2952-1662","authenticated-orcid":false,"given":"Yesul","family":"Jeong","sequence":"first","affiliation":[{"name":"Department of Hospital Pathology, St. Vincent\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sungman","family":"Hong","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, College of Medicine, Korea University, Seoul 02841, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sangjeong","family":"Ahn","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, College of Medicine, Korea University, Seoul 02841, Republic of Korea"},{"name":"Department of Pathology, College of Medicine, Korea University, Seoul 02841, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1020-5838","authenticated-orcid":false,"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, Seoul 06591, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,17]]},"reference":[{"key":"ref_1","first-page":"e70090","article-title":"Global cancer statistics 2024: GLOBOCAN estimates of incidence and mortality worldwide for 34 cancers in 186 countries","volume":"76","author":"Sung","year":"2026","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1186\/s12967-020-02554-y","article-title":"Endometrial cancer: Mapping the global landscape of research","volume":"18","author":"Ouassou","year":"2020","journal-title":"J. Transl. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"217","DOI":"10.6004\/jnccn.2022.7254","article-title":"Current Evidence-Based Systemic Therapy for Advanced and Recurrent Endometrial Cancer","volume":"21","author":"Gordhandas","year":"2023","journal-title":"J. Natl. Compr. Cancer Netw."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1038\/nature12113","article-title":"Integrated genomic characterization of endometrial carcinoma","volume":"497","author":"Levine","year":"2013","journal-title":"Nature"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"836","DOI":"10.1038\/modpathol.2015.43","article-title":"Refining prognosis and identifying targetable pathways for high-risk endometrial cancer; A TransPORTEC initiative","volume":"28","author":"Stelloo","year":"2015","journal-title":"Mod. Pathol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1038\/bjc.2015.190","article-title":"A clinically applicable molecular-based classification for endometrial cancers","volume":"113","author":"Talhouk","year":"2015","journal-title":"Br. J. Cancer"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4215","DOI":"10.1158\/1078-0432.CCR-15-2878","article-title":"Improved Risk Assessment by Integrating Molecular and Clinicopathological Factors in Early-stage Endometrial Cancer-Combined Analysis of the PORTEC Cohorts","volume":"22","author":"Stelloo","year":"2016","journal-title":"Clin. Cancer Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1002\/cncr.30496","article-title":"Confirmation of ProMisE: A simple, genomics-based clinical classifier for endometrial cancer","volume":"123","author":"Talhouk","year":"2017","journal-title":"Cancer"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2853","DOI":"10.1002\/cncr.34328","article-title":"Molecular classification in endometrial cancer: Opportunities for precision oncology in a changing landscape","volume":"128","author":"Jamieson","year":"2022","journal-title":"Cancer"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"210","DOI":"10.6004\/jnccn.2022.7096","article-title":"Molecular Profiling of Endometrial Cancer from TCGA to Clinical Practice","volume":"21","author":"Jamieson","year":"2023","journal-title":"J. Natl. Compr. Cancer Netw."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"703","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":"ref_12","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1038\/s41571-025-00991-6","article-title":"Artificial intelligence in digital pathology\u2014Time for a reality check","volume":"22","author":"Aggarwal","year":"2025","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"430","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":"ref_14","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1002\/ijgo.70094","article-title":"Artificial intelligence in the diagnosis and management of gynecologic cancer","volume":"171","author":"Paiboonborirak","year":"2025","journal-title":"Int. J. Gynaecol. Obstet."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"100940","DOI":"10.1016\/j.landig.2025.100940","article-title":"Artificial intelligence in women\u2019s cancers: Innovation and challenges in clinical translation","volume":"7","author":"Rockall","year":"2025","journal-title":"Lancet Digit. Health"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3942","DOI":"10.1038\/s41467-024-48062-1","article-title":"VOLTA: An enVironment-aware cOntrastive ceLl represenTation leArning for histopathology","volume":"15","author":"Nakhli","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1962","DOI":"10.1038\/s41591-024-02993-w","article-title":"Prediction of recurrence risk in endometrial cancer with multimodal deep learning","volume":"30","author":"Horeweg","year":"2024","journal-title":"Nat. Med."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"100400","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":"ref_19","doi-asserted-by":"crossref","first-page":"e71","DOI":"10.1016\/S2589-7500(22)00210-2","article-title":"Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: A combined analysis of the PORTEC randomised trials and clinical cohorts","volume":"5","author":"Fremond","year":"2023","journal-title":"Lancet Digit. Health"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"8877","DOI":"10.1007\/s00432-023-04838-4","article-title":"Deep learning-based methods for classification of microsatellite instability in endometrial cancer from HE-stained pathological images","volume":"149","author":"Zhang","year":"2023","journal-title":"J. Cancer Res. Clin. Oncol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1038\/s41746-024-01131-7","article-title":"Deep learning to assess microsatellite instability directly from histopathological whole slide images in endometrial cancer","volume":"7","author":"Wang","year":"2024","journal-title":"npj Digit. Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1674","DOI":"10.1007\/s10278-024-00997-z","article-title":"Predicting Mismatch Repair Deficiency Status in Endometrial Cancer through Multi-Resolution Ensemble Learning in Digital Pathology","volume":"37","author":"Whangbo","year":"2024","journal-title":"J. Imaging Inform. Med."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Umemoto, M., Mariya, T., Nambu, Y., Nagata, M., Horimai, T., Sugita, S., Kanaseki, T., Takenaka, Y., Shinkai, S., and Matsuura, M. (2024). Prediction of Mismatch Repair Status in Endometrial Cancer from Histological Slide Images Using Various Deep Learning-Based Algorithms. Cancers, 16.","DOI":"10.20944\/preprints202403.0474.v1"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1038\/s41698-024-00766-9","article-title":"Deep learning for endometrial cancer subtyping and predicting tumor mutational burden from histopathological slides","volume":"8","author":"Wang","year":"2024","journal-title":"npj Precis. Oncol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4973","DOI":"10.1038\/s41467-024-49017-2","article-title":"AI-based histopathology image analysis reveals a distinct subset of endometrial cancers","volume":"15","author":"Darbandsari","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"102099","DOI":"10.1016\/j.xcrm.2025.102099","article-title":"MMRNet: Ensemble deep learning models for predicting mismatch repair deficiency in endometrial cancer from histopathological images","volume":"6","author":"Liu","year":"2025","journal-title":"Cell Rep. Med."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"102499","DOI":"10.1016\/j.compmedimag.2025.102499","article-title":"Interpretable multi-stage attention network to predict cancer subtype, microsatellite instability, TP53 mutation and TMB of endometrial and colorectal cancer","volume":"121","author":"Wang","year":"2025","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1186\/s43556-025-00341-z","article-title":"Clinical-grade AI model for molecular subtyping of endometrial cancer: A multi-center cohort study in China","volume":"6","author":"Qi","year":"2025","journal-title":"Mol. Biomed."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"btaf059","DOI":"10.1093\/bioinformatics\/btaf059","article-title":"Prediction of molecular subtypes for endometrial cancer based on hierarchical foundation model","volume":"41","author":"Cui","year":"2025","journal-title":"Bioinformatics"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"103372","DOI":"10.1016\/j.media.2024.103372","article-title":"Ensemble transformer-based multiple instance learning to predict pathological subtypes and tumor mutational burden from histopathological whole slide images of endometrial and colorectal cancer","volume":"99","author":"Wang","year":"2025","journal-title":"Med. Image Anal."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"107497","DOI":"10.1016\/j.bspc.2025.107497","article-title":"Deep learning to identify aggressive endometrial cancer and predict tumor mutational burden from histopathology slide","volume":"104","author":"Wang","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5940","DOI":"10.1016\/j.acra.2025.07.050","article-title":"A Novel Model for Predicting Microsatellite Instability in Endometrial Cancer: Integrating Deep Learning-Pathomics and MRI-Based Radiomics","volume":"32","author":"Zhou","year":"2025","journal-title":"Acad. Radiol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lee, J.-O., Kim, C.Y., Lee, S., and Chung, J.-H. (2025). Multi-cancer analysis of histopathologic MSI screening based on digital histology image. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0332034"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1038\/s41698-026-01402-4","article-title":"Real-world benchmarking and validation of foundation model transformers for endometrial cancer subtyping from histopathology","volume":"10","author":"Wagner","year":"2026","journal-title":"npj Precis. Oncol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1038\/s41698-026-01522-x","article-title":"AI-assisted histomorphological stratification of endometrial cancer: Real-world validation of foundation models for molecular subtyping","volume":"10","author":"Schwarzmann","year":"2026","journal-title":"npj Precis. Oncol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1038\/s41698-026-01280-w","article-title":"An interpretable deep learning model for predicting endometrial cancer molecular subtypes from H&E-stained slides","volume":"10","author":"Guo","year":"2026","journal-title":"npj Precis. Oncol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1016\/j.annonc.2022.05.009","article-title":"Endometrial cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up","volume":"33","author":"Oaknin","year":"2022","journal-title":"Ann. Oncol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.3332\/ecancer.2020.1061","article-title":"Clinicopathologic features of endometrial cancer with mismatch repair deficiency","volume":"14","author":"Gordhandas","year":"2020","journal-title":"Ecancermedicalscience"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/s11864-017-0504-y","article-title":"Immunotherapy in Gynecologic Cancers: Are We There Yet?","volume":"18","author":"Pakish","year":"2017","journal-title":"Curr. Treat. Options Oncol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.humpath.2007.05.022","article-title":"Routinely assessed morphological features correlate with microsatellite instability status in endometrial cancer","volume":"39","author":"Shia","year":"2008","journal-title":"Hum. Pathol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1054","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":"ref_42","doi-asserted-by":"crossref","first-page":"1650","DOI":"10.1016\/j.ccell.2023.08.002","article-title":"Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study","volume":"41","author":"Wagner","year":"2023","journal-title":"Cancer Cell"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.ygyno.2015.01.541","article-title":"Detection of DNA mismatch repair (MMR) deficiencies by immunohistochemistry can effectively diagnose the microsatellite instability (MSI) phenotype in endometrial carcinomas","volume":"137","author":"McConechy","year":"2015","journal-title":"Gynecol. Oncol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"293","DOI":"10.2353\/jmoldx.2008.080031","article-title":"Immunohistochemistry versus microsatellite instability testing for screening colorectal cancer patients at risk for hereditary nonpolyposis colorectal cancer syndrome. Part I. The utility of immunohistochemistry","volume":"10","author":"Shia","year":"2008","journal-title":"J. Mol. Diagn."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"e423","DOI":"10.1016\/S1470-2045(25)00167-6","article-title":"ESGO-ESTRO-ESP guidelines for the management of patients with endometrial carcinoma: Update 2025","volume":"26","author":"Concin","year":"2025","journal-title":"Lancet Oncol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1002\/path.5375","article-title":"p53 immunohistochemistry is an accurate surrogate for TP53 mutational analysis in endometrial carcinoma biopsies","volume":"250","author":"Singh","year":"2020","journal-title":"J. Pathol."},{"key":"ref_47","unstructured":"Matias-Guiu, X., Lax, S.F., and WHO Classification of Tumours Editorial Board (2020). Endometrioid carcinoma of the uterine corpus. Female Genital Tumours, International Agency for Research on Cancer. [5th ed.]."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"S123","DOI":"10.1097\/PGP.0000000000000488","article-title":"Interpretation of P53 Immunohistochemistry in Endometrial Carcinomas: Toward Increased Reproducibility","volume":"38","author":"Ronnett","year":"2019","journal-title":"Int. J. Gynecol. Pathol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1097\/PGP.0000000000000725","article-title":"The Many Uses of p53 Immunohistochemistry in Gynecological Pathology: Proceedings of the ISGyP Companion Society Session at the 2020 USCAP Annual9 Meeting","volume":"40","author":"Kang","year":"2021","journal-title":"Int. J. Gynecol. Pathol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"S64","DOI":"10.1097\/PGP.0000000000000518","article-title":"Endometrial Carcinoma Diagnosis: Use of FIGO Grading and Genomic Subcategories in Clinical Practice: Recommendations of the International Society of Gynecological Pathologists","volume":"38","author":"Soslow","year":"2019","journal-title":"Int. J. Gynecol. Pathol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1002\/cjp2.243","article-title":"Selection of endometrial carcinomas for p53 immunohistochemistry based on nuclear features","volume":"8","author":"Kang","year":"2022","journal-title":"J. Pathol. Clin. Res."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.1038\/s41379-022-01102-x","article-title":"p53 immunohistochemistry in endometrial cancer: Clinical and molecular correlates in the PORTEC-3 trial","volume":"35","author":"Vermij","year":"2022","journal-title":"Mod. Pathol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"e20200100","DOI":"10.1590\/1678-4685-gmb-2020-0100","article-title":"First description of ultramutated endometrial cancer caused by germline loss-of-function and somatic exonuclease domain mutations in POLE gene","volume":"43","author":"Rosa","year":"2020","journal-title":"Genet. Mol. Biol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1038\/modpathol.2014.143","article-title":"Clinicopathological analysis of endometrial carcinomas harboring somatic POLE exonuclease domain mutations","volume":"28","author":"Hussein","year":"2015","journal-title":"Mod. Pathol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1442441","DOI":"10.1155\/2022\/1442441","article-title":"The Role of CTNNB1 in Endometrial Cancer","volume":"2022","author":"Ledinek","year":"2022","journal-title":"Dis. Markers"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1186\/s13073-017-0424-2","article-title":"Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden","volume":"9","author":"Chalmers","year":"2017","journal-title":"Genome Med."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1146\/annurev-cancerbio-061521-092038","article-title":"AI in Computational Pathology of Cancer: Improving Diagnostic Workflows and Clinical Outcomes?","volume":"7","author":"Cifci","year":"2023","journal-title":"Annu. Rev. Cancer Biol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.1038\/s41467-021-21896-9","article-title":"Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes","volume":"12","author":"Diao","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2924","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":"ref_60","doi-asserted-by":"crossref","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"e082505","DOI":"10.1136\/bmj-2024-082505","article-title":"PROBAST+AI: An updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods","volume":"388","author":"Moons","year":"2025","journal-title":"BMJ"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"3283","DOI":"10.1038\/s41591-025-03953-8","article-title":"The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence","volume":"31","author":"Sounderajah","year":"2025","journal-title":"Nat. Med."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"3749","DOI":"10.1038\/s41591-025-03982-3","article-title":"A multimodal whole-slide foundation model for pathology","volume":"31","author":"Ding","year":"2025","journal-title":"Nat. Med."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1038\/s41586-024-07441-w","article-title":"A whole-slide foundation model for digital pathology from real-world data","volume":"630","author":"Xu","year":"2024","journal-title":"Nature"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1038\/s41551-020-0578-x","article-title":"Integrating spatial gene expression and breast tumour morphology via deep learning","volume":"4","author":"He","year":"2020","journal-title":"Nat. Biomed. Eng."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1038\/s41746-025-02020-3","article-title":"STPath: A generative foundation model for integrating spatial transcriptomics and whole-slide images","volume":"8","author":"Huang","year":"2025","journal-title":"npj Digit. Med."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1038\/s42256-021-00303-4","article-title":"Morphological and molecular breast cancer profiling through explainable machine learning","volume":"3","author":"Binder","year":"2021","journal-title":"Nat. Mach. Intell."}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/27\/16\/7341\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T12:13:13Z","timestamp":1786968793000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/27\/16\/7341"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,17]]},"references-count":67,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["ijms27167341"],"URL":"https:\/\/doi.org\/10.3390\/ijms27167341","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,17]]}}}