{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T16:31:54Z","timestamp":1785429114652,"version":"3.56.0"},"reference-count":43,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T00:00:00Z","timestamp":1782518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000294","name":"Medical Research Scotland","doi-asserted-by":"publisher","award":["PHD-50538-2022"],"award-info":[{"award-number":["PHD-50538-2022"]}],"id":[{"id":"10.13039\/501100000294","id-type":"DOI","asserted-by":"publisher"}]},{"award":["PHD-50538-2022"],"award-info":[{"award-number":["PHD-50538-2022"]}],"id":[{"id":"https:\/\/ror.org\/00saj4962","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Background: Analysis of digital images of histopathological sections is increasing due to widespread adoption of fully digitised workflows and the greater availability of whole-slide scanners. Currently, hormone receptor status in breast carcinoma is assessed by pathologists scoring separate immunohistochemically stained sections. Methods: In this study, we employed pathologist-verified pixel-level annotations to train nested pixel classifiers capable of making case-level predictions of oestrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status directly from H&amp;E-stained sections using biopsy cases alone. The model was evaluated on both an internal test set and an external international evaluation set from an institution in a different continent using different scanner hardware without the need for image normalisation. Results: In the internal test set, the models achieved AUCs of 0.8030, 0.7956 and 0.7488 for ER, PR, and HER2, respectively, with AUCs of 0.7008 and 0.7488 for ER and PR using an external cohort from an institution from which no cases were used for training. Conclusions: Our data highlight a potential strategy by which a pixel-based classifier, typically developed to quantify histological features within individual cases, could be used to make case\/slide-level predictions but illustrate the challenges associated with this approach.<\/jats:p>","DOI":"10.3390\/cancers18132085","type":"journal-article","created":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T02:16:44Z","timestamp":1782699404000},"page":"2085","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Breast Cancer Hormone Receptor Status Determination from H&amp;E-Stained Biopsy Images Using Pixel-Level Classifiers"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-6075-9179","authenticated-orcid":false,"given":"Shuyang","family":"Wu","sequence":"first","affiliation":[{"name":"Centre for Inflammation Research, Institute of Regeneration and Repair, University of Edinburgh, Edinburgh EH16 4UU, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ines P.","family":"Nearchou","sequence":"additional","affiliation":[{"name":"Indica Labs, LCC, 8700 Education Pl NW, Bldg. B, Albuquerque, NM 87114, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0140-7115","authenticated-orcid":false,"given":"Sandrine","family":"Prost","sequence":"additional","affiliation":[{"name":"Centre for Inflammation Research, Institute of Regeneration and Repair, University of Edinburgh, Edinburgh EH16 4UU, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5741-1471","authenticated-orcid":false,"given":"Jonathan A.","family":"Fallowfield","sequence":"additional","affiliation":[{"name":"Centre for Inflammation Research, Institute of Regeneration and Repair, University of Edinburgh, Edinburgh EH16 4UU, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hideki","family":"Ueno","sequence":"additional","affiliation":[{"name":"Department of Surgery, National Defense Medical College, 3-2 Namiki, Tokorozawa 359-0042, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hitoshi","family":"Tsuda","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Pathology, Seikei-kai Chiba Medical Centre, 1-7-1 Minami-cho, Chuo-ku, Chiba 260-0842, Japan"},{"name":"Department of Basic Pathology, National Defense Medical College, 3-2 Namiki, Tokorozawa 359-0042, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alastair","family":"Ironside","sequence":"additional","affiliation":[{"name":"Directorate of Laboratory Medicine\u2014Pathology Department, NHS Lothian University Hospitals Division, Western General Hospital, Edinburgh EH4 2XU, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9041-9988","authenticated-orcid":false,"given":"David J.","family":"Harrison","sequence":"additional","affiliation":[{"name":"School of Medicine, University of St Andrews, St Andrews KY16 9AJ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4174-2786","authenticated-orcid":false,"given":"Timothy J.","family":"Kendall","sequence":"additional","affiliation":[{"name":"Centre for Inflammation Research, Institute of Regeneration and Repair, University of Edinburgh, Edinburgh EH16 4UU, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.breast.2022.08.010","article-title":"Current and future burden of breast cancer: Global statistics for 2020 and 2040","volume":"66","author":"Arnold","year":"2022","journal-title":"Breast"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1154","DOI":"10.1038\/s41591-025-03502-3","article-title":"Global patterns and trends in breast cancer incidence and mortality across 185 countries","volume":"31","author":"Kim","year":"2025","journal-title":"Nat. Med."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Rej, R.K., Roy, J., and Allu, S.R. (2024). Therapies for the Treatment of Advanced\/Metastatic Estrogen Receptor-Positive Breast Cancer: Current Situation and Future Directions. Cancers, 16.","DOI":"10.3390\/cancers16030552"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1038\/s41379-020-00704-7","article-title":"Prognostic and predictive parameters in breast pathology: A pathologist\u2019s primer","volume":"34","author":"Allison","year":"2021","journal-title":"Mod. Pathol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.1016\/j.annonc.2021.09.019","article-title":"ESMO Clinical Practice Guideline for the diagnosis, staging and treatment of patients with metastatic breast cancer","volume":"32","author":"Committee","year":"2021","journal-title":"Ann. Oncol."},{"key":"ref_6","unstructured":"Curigliano, G., Castelo-Branco, L., Gennari, A., Harbeck, N., Criscitiello, C., Trapani, D., Martins-Branco, D., Pentheroudakis, G., Curigliano, G., and Harbeck, N. (2025, April 01). Breast Cancer Types: What Your Type Means. Available online: https:\/\/www.esmo.org\/guidelines\/living-guidelines\/esmo-living-guideline-metastatic-breast-cancer."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"S8","DOI":"10.1038\/modpathol.2008.34","article-title":"Current issues in ER and HER2 testing by IHC in breast cancer","volume":"21","author":"Gown","year":"2008","journal-title":"Mod. Pathol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e197700","DOI":"10.1001\/jamanetworkopen.2019.7700","article-title":"Artificial Intelligence Algorithms to Assess Hormonal Status from Tissue Microarrays in Patients with Breast Cancer","volume":"2","author":"Shamai","year":"2019","journal-title":"JAMA Netw. Open"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1186\/s13058-024-01784-y","article-title":"Augmented interpretation of HER2, ER, and PR in breast cancer by artificial intelligence analyzer: Enhancing interobserver agreement through a reader study of 201 cases","volume":"26","author":"Jung","year":"2024","journal-title":"Breast Cancer Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e70051","DOI":"10.1002\/2056-4538.70051","article-title":"Inter-rater agreement of HER2-low scores between expert breast pathologists and the Visiopharm digital image analysis application (HER2 APP, CE2797)","volume":"11","author":"Parry","year":"2025","journal-title":"J. Pathol. Clin. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1038\/ng.2764","article-title":"The Cancer Genome Atlas Pan-Cancer analysis project","volume":"45","author":"Chang","year":"2013","journal-title":"Nat. Genet."},{"key":"ref_12","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":"Veta","year":"2017","journal-title":"JAMA"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"9910471","DOI":"10.1155\/2022\/9910471","article-title":"CAMELYON 17 Challenge: A Comparison of Traditional Machine Learning (SVM) with the Deep Learning Method","volume":"2022","author":"Sun","year":"2022","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"100933","DOI":"10.1016\/j.landig.2025.100933","article-title":"Application of artificial intelligence and digital tools in cancer pathology","volume":"7","author":"Shaktah","year":"2025","journal-title":"Lancet Digit. Health"},{"key":"ref_15","unstructured":"Indica Labs (2022, April 02). HALO. Available online: https:\/\/www.indicalab.com\/halo\/."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7216","DOI":"10.1038\/s41467-021-27451-w","article-title":"Nuclear pore protein NUP210 depletion suppresses metastasis through heterochromatin-mediated disruption of tumor cell mechanical response","volume":"12","author":"Amin","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Tumor Profiler consortium, Sobottka, B., Nowak, M., Frei, A.L., Haberecker, M., Merki, S., Levesque, M.P., Dummer, R., Moch, H., and Koelzer, V.H. (2021). Establishing standardized immune phenotyping of metastatic melanoma by digital pathology. Lab. Investig., 101, 1561\u20131570.","DOI":"10.1038\/s41374-021-00676-5"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ili\u00e9, M., Benzaquen, J., Tourniaire, P., Heeke, S., Ayache, N., Delingette, H., Long-Mira, E., Lassalle, S., Hamila, M., and Fayada, J. (2022). Deep Learning Facilitates Distinguishing Histologic Subtypes of Pulmonary Neuroendocrine Tumors on Digital Whole-Slide Images. Cancers, 14.","DOI":"10.3390\/cancers14071740"},{"key":"ref_19","unstructured":"(2022, June 15). International Collboration on Cancer Reporting. Invasive Carcinoma of the Breast. Available online: https:\/\/www.iccr-cancer.org\/datasets\/published-datasets\/breast\/invasive-carcinoma-of-the-breast\/."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1093\/annonc\/mds238","article-title":"A critical review why assessment of steroid hormone receptors in breast cancer should be quantitative","volume":"24","author":"Brouckaert","year":"2013","journal-title":"Ann. Oncol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1038\/s42256-023-00635-3","article-title":"Deep learning supported discovery of biomarkers for clinical prognosis of liver cancer","volume":"5","author":"Liang","year":"2023","journal-title":"Nat. Mach. Intell."},{"key":"ref_23","unstructured":"Wu, S., Qiu, Y., Nearchou, I.P., Prost, S., Fallowfield, J.A., Ueno, H., Tsuda, H., Harrison, D.J., Bilen, H., and Kendall, T.J. (2025). LadderMIL: Multiple Instance Learning with Coarse-to-Fine Self-Distillation. arXiv."},{"key":"ref_24","unstructured":"Shao, D., Chen, R.J., Song, A.H., Runevic, J., Lu, M.Y., Ding, T., and Mahmood, F. (2025, January 13\u201319). Do Multiple Instance Learning Models Transfer?. Proceedings of the 42nd International Conference on Machine Learning, Vancouver, BC, Canada."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1002\/path.6263","article-title":"AI-guided histopathology predicts brain metastasis in lung cancer patients","volume":"263","author":"Zhou","year":"2024","journal-title":"J. Pathol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.1038\/s41598-024-83267-w","article-title":"Impact of stain variation and color normalization for prognostic predictions in pathology","volume":"15","author":"Lin","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"101997","DOI":"10.1016\/j.inffus.2023.101997","article-title":"Stain normalization methods for histopathology image analysis: A comprehensive review and experimental comparison","volume":"102","author":"Hoque","year":"2024","journal-title":"Inf. Fusion"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"863","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":"ref_29","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_30","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_31","doi-asserted-by":"crossref","first-page":"2429","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_32","doi-asserted-by":"crossref","first-page":"865","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":"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":"800","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":"ref_35","doi-asserted-by":"crossref","unstructured":"Crispino, A., Varricchio, S., Marfella, A., Cerbone, D., Russo, D., Di Crescenzo, R.M., Staibano, S., Merolla, F., and Ilardi, G. (2025). A Weakly Supervised Approach for HPV Status Prediction in Oropharyngeal Carcinoma from H&E-Stained Slides. Cancers, 17.","DOI":"10.20944\/preprints202511.0812.v1"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Kiraz, U., Fernandez-Martin, C., Rewcastle, E., Gudlaugsson, E.G., Skaland, I., Naranjo, V., Morales-Martinez, S., and Janssen, E.A.M. (2025). Validation of an Artificial Intelligence Model for Breast Cancer Molecular Subtyping Using Hematoxylin and Eosin-Stained Whole-Slide Images in a Population-Based Cohort. Cancers, 17.","DOI":"10.3390\/cancers17193234"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Jiao, P., Zheng, Q., Yang, R., Ni, X., Wu, J., Chen, Z., and Liu, X. (2024). Prediction of HER2 Status Based on Deep Learning in H&E-Stained Histopathology Images of Bladder Cancer. Biomedicines, 12.","DOI":"10.3390\/biomedicines12071583"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Pettersen, H.S., Belevich, I., R\u00f8yset, E.S., Smistad, E., Simpson, M.R., Jokitalo, E., Reinertsen, I., Bakke, I., and Pedersen, A. (2022). Code-Free Development and Deployment of Deep Segmentation Models for Digital Pathology. Front. Med., 8.","DOI":"10.3389\/fmed.2021.816281"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Sekhar, A., Goel, V., Jain, G., Patil, A., Gupta, R.K., Bameta, T., Rane, S., and Sethi, A. (2024, January 27\u201329). HER2 and Fish Status Prediction in Breast Biopsy H&E-Stained Images Using Deep Learning. Proceedings of the 2024 IEEE 24th International Conference on Bioinformatics and Bioengineering (BIBE), Kragujevac, Serbia.","DOI":"10.1109\/BIBE63649.2024.10820489"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"17507","DOI":"10.1038\/s41598-020-74500-3","article-title":"SHIFT: Speedy histological-to-immunofluorescent translation of a tumor signature enabled by deep learning","volume":"10","author":"Burlingame","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1977","DOI":"10.1109\/TMI.2021.3069874","article-title":"Unpaired Stain Transfer Using Pathology-Consistent Constrained Generative Adversarial Networks","volume":"40","author":"Liu","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"100410","DOI":"10.1016\/j.jpi.2024.100410","article-title":"Deep learning-based classification of breast cancer molecular subtypes from H&E whole-slide images","volume":"16","author":"Tafavvoghi","year":"2025","journal-title":"J. Pathol. Inform."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"21454","DOI":"10.1038\/s41598-023-48830-x","article-title":"Deep learning-enabled breast cancer endocrine response determination from H&E staining based on ESR1 signaling activity","volume":"13","author":"Ng","year":"2023","journal-title":"Sci. Rep."}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/18\/13\/2085\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T02:38:56Z","timestamp":1782700736000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/18\/13\/2085"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,27]]},"references-count":43,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["cancers18132085"],"URL":"https:\/\/doi.org\/10.3390\/cancers18132085","relation":{},"ISSN":["2072-6694"],"issn-type":[{"value":"2072-6694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,27]]}}}