Shephard, A.J. orcid.org/0000-0003-0969-2990, Mahmood, H. orcid.org/0000-0001-7159-0368, Raza, S.E.A. orcid.org/0000-0002-1097-1738 et al. (2 more authors) (2025) A novel AI-based score for assessing the prognostic value of intra-epithelial lymphocytes in oral epithelial dysplasia. British Journal of Cancer, 132 (2). pp. 168-179. ISSN 0007-0920
Abstract
Background
Oral epithelial dysplasia (OED) poses a significant clinical challenge due to its potential for malignant transformation and the lack of reliable prognostic markers. Current OED grading systems do not reliably predict transformation and suffer from considerable observer variability. Recent studies have highlighted that peri-epithelial lymphocytes may play an important role in OED malignant transformation, with indication that intra-epithelial lymphocytes (IELs) may also be important.
Methods
We propose a novel artificial intelligence (AI) based IEL score from Haematoxylin and Eosin (H&E) stained Whole Slide Images (WSIs) of OED tissue slides. We determine the prognostic value of our IEL score on a digital dataset of 219 OED WSIs (acquired using three different scanners), compared to pathologist-led clinical grading.
Results
Our IEL scores demonstrated significant prognostic value (C-index = 0.67, p < 0.001) and were shown to improve both the binary/WHO grading systems in multivariate analyses (p < 0.001). Nuclear analyses confirmed the positive association between higher IEL scores, more severe OED and malignant transformation (p < 0.05).
Conclusions
This underscores the potential importance of IELs, and by extension our IEL score, as prognostic indicators in OED. Further validation through prospective multi-centric studies is warranted to confirm the clinical utility of IELs.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
Keywords: | Computer science; Oral cancer detection; Prognostic markers |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Clinical Dentistry (Sheffield) |
Funding Information: | Funder Grant number NIHR Academy NIHR300904 Cancer Research UK 29674 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 10 Jan 2025 16:43 |
Last Modified: | 27 Jan 2025 09:48 |
Status: | Published |
Publisher: | Springer Science and Business Media LLC |
Refereed: | Yes |
Identification Number: | 10.1038/s41416-024-02916-z |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:221540 |