Westwood, A.C., Wilson, B.I., Laye, J. et al. (5 more authors) (2025) Deep-learning enabled combined measurement of tumour cell density and tumour infiltrating lymphocyte density as a prognostic biomarker in colorectal cancer. BJC Reports, 3. 12. ISSN: 2731-9377
Abstract
Background
Within the colorectal cancer (CRC) tumour microenvironment, tumour infiltrating lymphocytes (TILs) and tumour cell density (TCD) are recognised prognostic markers. Measurement of TILs and TCD using deep-learning (DL) on haematoxylin and eosin (HE) whole slide images (WSIs) could aid management.
Methods
HE WSIs from the primary tumours of 127 CRC patients were included. DL was used to quantify TILs across different regions of the tumour and TCD at the luminal surface. The relationship between TILs, TCD, and cancer-specific survival was analysed.
Results
Median TIL density was higher at the invasive margin than the luminal surface (963 vs 795 TILs/mm2, P = 0.010). TILs and TCD were independently prognostic in multivariate analyses (HR 4.28, 95% CI 1.87–11.71, P = 0.004; HR 2.72, 95% CI 1.19–6.17, P = 0.017, respectively). Patients with both low TCD and low TILs had the poorest survival (HR 10.0, 95% CI 2.51–39.78, P = 0.001), when compared to those with a high TCD and TILs score.
Conclusions
DL derived TIL and TCD score were independently prognostic in CRC. Patients with low TILs and TCD are at the highest risk of cancer-specific death. DL quantification of TILs and TCD could be used in combination alongside other validated prognostic biomarkers in routine clinical practice.
Metadata
| Item Type: | Article |
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| Authors/Creators: |
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| Copyright, Publisher and Additional Information: | © The Author(s) 2025. 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/. |
| Dates: |
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| Institution: | The University of Leeds |
| Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) |
| Funding Information: | Funder Grant number Yorkshire Cancer Research Account Ref: 2UOLEEDS L386-RA/2015/R2/003 Cancer Research UK Supplier No: 138573 RRCOER-Jun24/100004 NIHR National Inst Health Research Not Known |
| Date Deposited: | 12 Nov 2025 15:47 |
| Last Modified: | 12 Nov 2025 15:47 |
| Status: | Published |
| Publisher: | Springer Nature |
| Identification Number: | 10.1038/s44276-025-00123-8 |
| Related URLs: | |
| Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:233972 |
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