{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:20:02Z","timestamp":1760059202556,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T00:00:00Z","timestamp":1748304000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Tony Bramall Charitable Trust","award":["EP\/S024336\/1"],"award-info":[{"award-number":["EP\/S024336\/1"]}]},{"name":"UKRI Engineering and Physical Sciences Research Council (EPSRC)","award":["EP\/S024336\/1"],"award-info":[{"award-number":["EP\/S024336\/1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Background\/Objectives: Surgical pathology of tubo-ovarian and peritoneal cancer carries a well-recognised diagnostic workload, partly due to the large amount of non-primary tumour-related tissue requiring assessment for the presence of metastatic disease. The lymph nodes and omentum are almost universally included in such resection cases and contribute considerably to this burden, principally due to volume rather than task complexity. To date, artificial intelligence (AI)-based studies have reported good success rates in identifying nodal spread in other malignancies, but the development of such time-saving assistive digital solutions has been neglected in ovarian cancer. This study aimed to detect the presence or absence of metastatic ovarian carcinoma in the lymph nodes and omentum. Methods: We used attention-based multiple-instance learning (ABMIL) with a vision-transformer foundation model to classify whole-slide images (WSIs) as either containing ovarian carcinoma metastases or not. Training and validation were conducted with a total of 855 WSIs of surgical resection specimens collected from 404 patients at Leeds Teaching Hospitals NHS Trust. Results: Ensembled classification from hold-out testing reached an AUROC of 0.998 (0.985\u20131.0) and a balanced accuracy of 100% (100.0\u2013100.0%) in the lymph node set, and an AUROC of 0.963 (0.911\u20130.999) and a balanced accuracy of 98.0% (94.8\u2013100.0%) in the omentum set. Conclusions: This model shows great potential in the identification of ovarian carcinoma nodal and omental metastases, and could provide clinical utility through its ability to pre-screen WSIs prior to histopathologist review. In turn, this could offer significant time-saving benefits and streamline clinical diagnostic workflows, helping to address the chronic staffing shortages in histopathology.<\/jats:p>","DOI":"10.3390\/cancers17111789","type":"journal-article","created":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T11:12:57Z","timestamp":1748344377000},"page":"1789","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multiple Instance Learning for the Detection of Lymph Node and Omental Metastases in Carcinoma of the Ovaries, Fallopian Tubes and Peritoneum"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5861-3745","authenticated-orcid":false,"given":"Katie E.","family":"Allen","sequence":"first","affiliation":[{"name":"Leeds Institute of Medical Research, University of Leeds, Leeds LS9 7TF, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9020-3383","authenticated-orcid":false,"given":"Jack","family":"Breen","sequence":"additional","affiliation":[{"name":"School of Computing, University of Leeds, Leeds LS2 9JT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8864-5932","authenticated-orcid":false,"given":"Geoff","family":"Hall","sequence":"additional","affiliation":[{"name":"Leeds Cancer Centre, University of Leeds, Leeds LS9 7TF, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4959-294X","authenticated-orcid":false,"given":"Georgia","family":"Mappa","sequence":"additional","affiliation":[{"name":"Leeds Institute of Medical Research, University of Leeds, Leeds LS9 7TF, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4385-3153","authenticated-orcid":false,"given":"Kieran","family":"Zucker","sequence":"additional","affiliation":[{"name":"Leeds Cancer Centre, University of Leeds, Leeds LS9 7TF, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0134-107X","authenticated-orcid":false,"given":"Nishant","family":"Ravikumar","sequence":"additional","affiliation":[{"name":"School of Computing, University of Leeds, Leeds LS2 9JT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0890-0399","authenticated-orcid":false,"given":"Nicolas M.","family":"Orsi","sequence":"additional","affiliation":[{"name":"Leeds Institute of Medical Research, University of Leeds, Leeds LS9 7TF, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"229","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 A Cancer J. Clin."},{"key":"ref_2","unstructured":"(2025, February 13). Cancer Research UK Early Diagnosis Hub. Available online: https:\/\/crukcancerintelligence.shinyapps.io\/EarlyDiagnosis\/."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"102074","DOI":"10.1016\/j.canep.2021.102074","article-title":"Ovarian cancer survival by stage, histotype, and pre-diagnostic lifestyle factors, in the prospective UK Million Women Study","volume":"76","author":"Gaitskell","year":"2021","journal-title":"Cancer Epidemiol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"20210091","DOI":"10.1259\/bjr.20210091","article-title":"Ovarian cancer staging: What the surgeon needs to know","volume":"94","author":"Horvat","year":"2021","journal-title":"Br. J. Radiol."},{"key":"ref_5","unstructured":"(2025, February 13). The Royal College of Pathologists Meeting Pathology Demand Histopathology Workforce Census. Available online: https:\/\/www.rcpath.org\/profession\/workforce-planning\/our-workforce-research\/histopathology-workforce-survey-2018.html."},{"key":"ref_6","first-page":"18","article-title":"Constant Demand, Patchy Supply","volume":"88","author":"Bychkov","year":"2023","journal-title":"Pathologist"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1186\/s13000-024-01590-2","article-title":"The current troubled state of the global pathology workforce: A concise review","volume":"19","author":"Walsh","year":"2024","journal-title":"Diagn. Pathol."},{"key":"ref_8","doi-asserted-by":"crossref","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":"2021","journal-title":"Mod. Pathol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1038\/s41698-023-00432-6","article-title":"Artificial intelligence in ovarian cancer histopathology: A systematic review","volume":"7","author":"Breen","year":"2023","journal-title":"NPJ Precis. Oncol."},{"key":"ref_10","unstructured":"Holback, C., Jarosz, R., Prior, F., Mutch, D.G., Bhosale, P., Garcia, K., Lee, Y., Kirk, S., Sadow, C.A., and Levine, S. (2025, February 13). The Cancer Genome Atlas Ovarian Cancer Collection (TCGA-OV) (Version 4) 2016, the Cancer Imaging Archive. Available online: https:\/\/www.cancerimagingarchive.net\/collection\/tcga-ov\/."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.ygyno.2021.07.015","article-title":"Integration of histopathological images and multi-dimensional omics analyses predicts molecular features and prognosis in high-grade serous ovarian cancer","volume":"163","author":"Zeng","year":"2021","journal-title":"Gynecol. Oncol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"100160","DOI":"10.1016\/j.jpi.2022.100160","article-title":"Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation","volume":"14","author":"Ho","year":"2022","journal-title":"J. Pathol. Inform."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Nero, C., Boldrini, L., Lenkowicz, J., Giudice, M.T., Piermattei, A., Inzani, F., Pasciuto, T., Minucci, A., Fagotti, A., and Zannoni, G. (2022). Deep-Learning to Predict BRCA Mutation and Survival from Digital H&E Slides of Epithelial Ovarian Cancer. Int. J. Mol. Sci., 23.","DOI":"10.3390\/ijms231911326"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"100872","DOI":"10.1016\/j.xcrm.2022.100872","article-title":"Deep learning identifies morphological patterns of homologous recombination deficiency in luminal breast cancers from whole slide images","volume":"3","author":"Lazard","year":"2022","journal-title":"Cell Rep. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1038\/s44294-024-00016-0","article-title":"Deep learning detects premalignant lesions in the Fallopian tube","volume":"2","author":"Bogaerts","year":"2024","journal-title":"NPJ Women\u2019s Health"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yaar, A., Asif, A., Ahmed Raza, S.E., Rajpoot, N., and Minhas, F. (2020, January 14\u201319). Cross-Domain Knowledge Transfer for Prediction of Chemosensitivity in Ovarian Cancer Patients. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00472"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yu, K.-H., Hu, V., Wang, F., Matulonis, U.A., Mutter, G.L., Golden, J.A., and Kohane, I.S. (2020). Deciphering serous ovarian carcinoma histopathology and platinum response by convolutional neural networks. BMC Med., 18.","DOI":"10.1186\/s12916-020-01684-w"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"102093","DOI":"10.1016\/j.compmedimag.2022.102093","article-title":"Weakly supervised deep learning for prediction of treatment effectiveness on ovarian cancer from histopathology images","volume":"99","author":"Wang","year":"2022","journal-title":"Comput. Med Imaging Graph."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ghoniem, R.M., Algarni, A.D., Refky, B., and Ewees, A.A. (2021). Multi-Modal Evolutionary Deep Learning Model for Ovarian Cancer Diagnosis. Symmetry, 13.","DOI":"10.3390\/sym13040643"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"100261","DOI":"10.1016\/j.labinv.2023.100261","article-title":"Real-World Implementation of Digital Pathology: Results from an Intercontinental Survey","volume":"103","author":"Pinto","year":"2023","journal-title":"Mod. Pathol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.ejca.2023.04.023","article-title":"Deep learning detection of melanoma metastases in lymph nodes","volume":"188","author":"Jansen","year":"2023","journal-title":"Eur. J. Cancer"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"113390","DOI":"10.1016\/j.ejca.2023.113390","article-title":"Deep learning to predict breast cancer sentinel lymph node status on INSEMA histological images","volume":"195","author":"Gerber","year":"2023","journal-title":"Eur. J. Cancer"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2199","DOI":"10.1001\/jama.2017.14585","article-title":"Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women with Breast Cancer","volume":"318","author":"Bejnordi","year":"2017","journal-title":"JAMA"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"100216","DOI":"10.1016\/j.modpat.2023.100216","article-title":"Artificial Intelligence\u2013Aided Diagnosis of Breast Cancer Lymph Node Metastasis on Histologic Slides in a Digital Workflow","volume":"36","author":"Challa","year":"2023","journal-title":"Mod. Pathol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1038\/s41591-019-0508-1","article-title":"Clinical-grade computational pathology using weakly supervised deep learning on whole slide images","volume":"25","author":"Campanella","year":"2019","journal-title":"Nat. Med."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1097\/PAS.0000000000002248","article-title":"Artificial Intelligence Helps Pathologists Increase Diagnostic Accuracy and Efficiency in the Detection of Breast Cancer Lymph Node Metastases","volume":"48","author":"Retamero","year":"2024","journal-title":"Am. J. Surg. Pathol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/S1470-2045(23)00061-X","article-title":"Artificial intelligence-based model for lymph node metastases detection on whole slide images in bladder cancer: A retrospective, multicentre, diagnostic study","volume":"24","author":"Wu","year":"2023","journal-title":"Lancet Oncol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"868","DOI":"10.1007\/s10120-021-01158-9","article-title":"Deep learning system for lymph node quantification and metastatic cancer identification from whole-slide pathology images","volume":"24","author":"Hu","year":"2021","journal-title":"Gastric Cancer"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3347","DOI":"10.1038\/s41467-022-30746-1","article-title":"Deep neural network trained on gigapixel images improves lymph node metastasis detection in clinical settings","volume":"13","author":"Huang","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_30","unstructured":"(2025, March 06). DeepPath Lydia. Available online: https:\/\/www.deeppath.io\/deeppath-lydia\/."},{"key":"ref_31","first-page":"A429","article-title":"#900 Comparative evaluation of ovarian carcinoma subtyping in primary versus interval debulking surgery specimen whole slide images using artificial intelligence","volume":"33","author":"Allen","year":"2023","journal-title":"Int. J. Gynecol. Cancer"},{"key":"ref_32","unstructured":"Dy, J., and Krause, A. (2018, January 10\u201315). Attention-Based Deep Multiple Instance Learning. Proceedings of the 35th International Conference on Machine Learning, Stockholm, Sweden."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"555","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":"ref_34","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1038\/s41698-025-00799-8","article-title":"A comprehensive evaluation of histopathology foundation models for ovarian cancer subtype classification","volume":"9","author":"Breen","year":"2025","journal-title":"NPJ Precis. Oncol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Breen, J., Ravikumar, N., Allen, K., Zucker, K., and Orsi, N.M. (2024, January 27\u201330). Reducing Histopathology Slide Magnification Improves the Accuracy and Speed of Ovarian Cancer Subtyping. Proceedings of the 2024 IEEE International Symposium on Biomedical Imaging (ISBI), Athens, Greece.","DOI":"10.1109\/ISBI56570.2024.10635499"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"850","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":"ref_37","doi-asserted-by":"crossref","first-page":"859","DOI":"10.5858\/arpa.2018-0147-OA","article-title":"Artificial Intelligence\u2013Based Breast Cancer Nodal Metastasis Detection: Insights into the Black Box for Pathologists","volume":"143","author":"Liu","year":"2018","journal-title":"Arch. Pathol. Lab. Med."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"984","DOI":"10.1097\/PAS.0b013e3181e1a3bb","article-title":"Diagnosis of Ovarian Carcinoma Cell Type is Highly Reproducible","volume":"34","author":"Kalloger","year":"2010","journal-title":"Am. J. Surg. Pathol."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Asadi-Aghbolaghi, M., Farahani, H., Zhang, A., Akbari, A., Kim, S., Chow, A., Dane, S., Huntsman, D.G., Gilks, C.B., and Ramus, S. (2024). Machine Learning-Driven Histotype Diagnosis of Ovarian Carcinoma: Insights from the OCEAN AI Challenge 2024. medRxiv, medRxiv:2024.04.19.24306099.","DOI":"10.1101\/2024.04.19.24306099"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ijgo.2013.10.001","article-title":"Staging classification for cancer of the ovary, fallopian tube, and peritoneum","volume":"124","author":"Prat","year":"2013","journal-title":"Int. J. Gynecol. Obstet."},{"key":"ref_41","unstructured":"Edge, S.B., Byrd, D.R., Compton, C.C., Fritz, A.G., and Greene, F.L. (2010). Trotti A AJCC Cancer Staging Manual, Springer. [7th ed.]."}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/17\/11\/1789\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:41:36Z","timestamp":1760031696000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/17\/11\/1789"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,27]]},"references-count":41,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["cancers17111789"],"URL":"https:\/\/doi.org\/10.3390\/cancers17111789","relation":{},"ISSN":["2072-6694"],"issn-type":[{"type":"electronic","value":"2072-6694"}],"subject":[],"published":{"date-parts":[[2025,5,27]]}}}