{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T13:15:58Z","timestamp":1785417358494,"version":"3.56.0"},"reference-count":27,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T00:00:00Z","timestamp":1778544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Health and Welfare","award":["RS-2021-KH113146"],"award-info":[{"award-number":["RS-2021-KH113146"]}]},{"award":["RS-2021-KH113146"],"award-info":[{"award-number":["RS-2021-KH113146"]}],"id":[{"id":"https:\/\/ror.org\/00vxgjw72","id-type":"ROR","asserted-by":"publisher"}]},{"name":"National Cancer Center","award":["RS-2023-CC138464"],"award-info":[{"award-number":["RS-2023-CC138464"]}]},{"award":["RS-2023-CC138464"],"award-info":[{"award-number":["RS-2023-CC138464"]}],"id":[{"id":"https:\/\/ror.org\/02tsanh21","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Ovarian epithelial cancers (EOCs) comprise heterogeneous subtypes with distinct clinical outcomes, making accurate histological subtyping essential for prognosis and treatment planning. Although deep learning using digitized hematoxylin and eosin (H&amp;E) whole-slide images (WSIs) is now widely used, its application to ovarian cancer diagnosis remains limited. Methods: In this multicenter study, we analyzed 319 H&amp;E-stained slides from 152 patients with surgically resected EOC. An attention-based multiple instance learning (MIL) framework built on a pathology-specific foundation model (UNI) was used. WSIs were divided into 512 \u00d7 512-pixel patches at 40\u00d7 magnification, and slide-level classification were generated through attention-based aggregation of patch-level feature, followed by patient-level prediction. External validation was performed specifically on the high-grade serous carcinoma (HGSC) cases from The Cancer Genome Atlas (TCGA) dataset. Results: The model achieved strong performance, with slide-level and patient-level accuracies of 0.918 and 0.900, respectively, on the test set. In five-fold cross-validation, the mean slide-level AUC was 0.990 (95% CI: 0.983\u20130.997), and the patient-level AUC was 0.993 (95% CI: 0.989\u20130.996), indicating consistent results. External validation on TCGA HGSC cases showed robust generalizability, with slide-level and patient-level accuracies of 0.794 and 0.898. F1-scores ranged from 0.832 to 1.000 at the slide-level and from 0.831 to 0.966 at the patient-level, with particularly strong performance for HGSC and clear-cell carcinoma. Conclusions: These findings demonstrate the feasibility of deep learning-based models for histological subtyping of EOC using H&amp;E-stained WSIs. This approach may help pathologists achieve more accurate and consistent histological diagnoses of EOC.<\/jats:p>","DOI":"10.3390\/diagnostics16101470","type":"journal-article","created":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:04:36Z","timestamp":1778601876000},"page":"1470","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning-Based Diagnosis of Epithelial Ovarian Cancer from Whole-Slide Histopathology Images"],"prefix":"10.3390","volume":"16","author":[{"given":"Jihyun","family":"Chun","sequence":"first","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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8980-7702","authenticated-orcid":false,"given":"Haeyoun","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Pathology, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam 13496, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heewon","family":"Chung","sequence":"additional","affiliation":[{"name":"AI Healthcare Innovation Team, MTS Company Inc., Seoul 06178, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae-Myung","family":"Jang","sequence":"additional","affiliation":[{"name":"AI Healthcare Innovation Team, MTS Company Inc., Seoul 06178, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jangwon","family":"Seo","sequence":"additional","affiliation":[{"name":"AI Healthcare Innovation Team, MTS Company Inc., Seoul 06178, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taegyu","family":"Kim","sequence":"additional","affiliation":[{"name":"AI Healthcare Innovation Team, MTS Company Inc., Seoul 06178, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Woohyun","family":"Lee","sequence":"additional","affiliation":[{"name":"AI Healthcare Innovation Team, MTS Company Inc., Seoul 06178, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheolhong","family":"Park","sequence":"additional","affiliation":[{"name":"AI Healthcare Innovation Team, MTS Company Inc., Seoul 06178, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingi","family":"Hong","sequence":"additional","affiliation":[{"name":"AI Healthcare Innovation Team, MTS Company Inc., Seoul 06178, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han-Mac Brian","family":"Kim","sequence":"additional","affiliation":[{"name":"AI Healthcare Innovation Team, MTS Company Inc., Seoul 06178, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3096-1112","authenticated-orcid":false,"given":"Messi H. J.","family":"Lee","sequence":"additional","affiliation":[{"name":"AI Healthcare Innovation Team, MTS Company Inc., Seoul 06178, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyongseok","family":"Jang","sequence":"additional","affiliation":[{"name":"AI Healthcare Innovation Team, MTS Company Inc., Seoul 06178, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6843-3708","authenticated-orcid":false,"given":"Chan Kwon","family":"Jung","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"},{"name":"Cancer Research Institute, The Catholic University of Korea, Seoul 06591, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8342-8701","authenticated-orcid":false,"given":"Sang Wun","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Institute of Women\u2019s Life Medical Science, Yonsei University College of Medicine, Seoul 03722, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2523-9531","authenticated-orcid":false,"given":"Ahwon","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"},{"name":"Cancer Research Institute, The Catholic University of Korea, Seoul 06591, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1038\/s41571-024-00881-3","article-title":"Global epidemiology of epithelial ovarian cancer","volume":"21","author":"Webb","year":"2024","journal-title":"Nat. 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