{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T15:49:12Z","timestamp":1792597752984,"version":"4.5.0"},"reference-count":37,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,5,18]],"date-time":"2025-05-18T00:00:00Z","timestamp":1747526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","award":["MOP-142198"],"award-info":[{"award-number":["MOP-142198"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cells"],"abstract":"<jats:p>Artificial intelligence (AI) is becoming an integral part of pathological assessment and diagnostic procedures in modern pathology. As most prostate cancers (PCa) arise from glandular epithelial tissue, an AI-based methodology has been developed to recognize glandular epithelial nuclei in prostate biopsy tissue. An integrated machine-learning network, named GlandNet, was developed to correctly recognize the epithelial cells within prostate glands using cell-centric patches selected from the core biopsy specimens. Feulgen-Thionin (a DNA stoichiometric label) was used to stain biopsy sections (4\u20137 \u00b5m in thickness) from 82 active surveillance patients diagnosed with PCa. Images of these sections were human-annotated, and the resultant dataset consisted of 1,264,772 segmented, cell-centric nuclei patches, of which 449,879 were centered on epithelial gland nuclei from 110 needle biopsies (training set: n = 66; validation set: n = 22; and test set: n = 22). The training of GlandNet used semi-supervised machine-learning knowledge of the training and validation cohorts and integrated both human and AI predictions to enhance its performance on the test cohort. The performance was evaluated against a consensus deliberation from three observers. The GlandNet demonstrated an average accuracy, sensitivity, specificity, and F1-score of 94.1%, 95.7%, 87.8%, and 95.2%, respectively, when tested on the 20,735 glandular cells found in the three needle biopsies with the visually best consensus predictions. Conversely, the average accuracy, sensitivity, specificity, and F1-score were 90.9%, 86.4%, 94.0%, and 89.7% when assessed on 57,217 cells found in the three needle biopsies with the visually worst consensus predictions. GlandNet is a first-generation AI with an excellent ability to differentiate between epithelial and stromal nuclei in core biopsies from patients with early prostate cancer.<\/jats:p>","DOI":"10.3390\/cells14100737","type":"journal-article","created":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T06:54:28Z","timestamp":1747724068000},"page":"737","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Recognizing Epithelial Cells in Prostatic Glands Using Deep Learning"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7606-3719","authenticated-orcid":false,"given":"Liton","family":"Devnath","sequence":"first","affiliation":[{"name":"Integrative Oncology, BC Cancer Research Centre, Vancouver, BC V5Z 1L3, Canada"},{"name":"Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 2B5, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Puneet","family":"Arora","sequence":"additional","affiliation":[{"name":"Integrative Oncology, BC Cancer Research Centre, Vancouver, BC V5Z 1L3, Canada"},{"name":"Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 2B5, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anita","family":"Carraro","sequence":"additional","affiliation":[{"name":"Integrative Oncology, BC Cancer Research Centre, Vancouver, BC V5Z 1L3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jagoda","family":"Korbelik","sequence":"additional","affiliation":[{"name":"Integrative Oncology, BC Cancer Research Centre, Vancouver, BC V5Z 1L3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mira","family":"Keyes","sequence":"additional","affiliation":[{"name":"BC Cancer Agency, Vancouver, BC V5Z 4E9, Canada"},{"name":"Radiation Oncology, University of British Columbia, Vancouver, BC V6T 1Z4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Wang","sequence":"additional","affiliation":[{"name":"Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 2B5, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martial","family":"Guillaud","sequence":"additional","affiliation":[{"name":"Integrative Oncology, BC Cancer Research Centre, Vancouver, BC V5Z 1L3, Canada"},{"name":"Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 2B5, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4440-2792","authenticated-orcid":false,"given":"Calum","family":"MacAulay","sequence":"additional","affiliation":[{"name":"Integrative Oncology, BC Cancer Research Centre, Vancouver, BC V5Z 1L3, Canada"},{"name":"Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 2B5, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"12","DOI":"10.3322\/caac.21820","article-title":"Cancer statistics","volume":"74","author":"Siegel","year":"2024","journal-title":"CA Cancer J. 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