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Evaluation of tumour infiltrating lymphocytes in luminal breast cancer using artificial intelligence

BACKGROUND: Tumour infiltrating lymphocytes (TILs) are a prognostic parameter in triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC). However, their role in luminal (oestrogen receptor positive and HER2 negative (ER + /HER2-)) BC remains unclear. In this s...

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Autores principales: Makhlouf, Shorouk, Wahab, Noorul, Toss, Michael, Ibrahim, Asmaa, Lashen, Ayat G., Atallah, Nehal M., Ghannam, Suzan, Jahanifar, Mostafa, Lu, Wenqi, Graham, Simon, Mongan, Nigel P., Bilal, Mohsin, Bhalerao, Abhir, Snead, David, Minhas, Fayyaz, Raza, Shan E. Ahmed, Rajpoot, Nasir, Rakha, Emad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10667537/
https://www.ncbi.nlm.nih.gov/pubmed/37777578
http://dx.doi.org/10.1038/s41416-023-02451-3
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author Makhlouf, Shorouk
Wahab, Noorul
Toss, Michael
Ibrahim, Asmaa
Lashen, Ayat G.
Atallah, Nehal M.
Ghannam, Suzan
Jahanifar, Mostafa
Lu, Wenqi
Graham, Simon
Mongan, Nigel P.
Bilal, Mohsin
Bhalerao, Abhir
Snead, David
Minhas, Fayyaz
Raza, Shan E. Ahmed
Rajpoot, Nasir
Rakha, Emad
author_facet Makhlouf, Shorouk
Wahab, Noorul
Toss, Michael
Ibrahim, Asmaa
Lashen, Ayat G.
Atallah, Nehal M.
Ghannam, Suzan
Jahanifar, Mostafa
Lu, Wenqi
Graham, Simon
Mongan, Nigel P.
Bilal, Mohsin
Bhalerao, Abhir
Snead, David
Minhas, Fayyaz
Raza, Shan E. Ahmed
Rajpoot, Nasir
Rakha, Emad
author_sort Makhlouf, Shorouk
collection PubMed
description BACKGROUND: Tumour infiltrating lymphocytes (TILs) are a prognostic parameter in triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC). However, their role in luminal (oestrogen receptor positive and HER2 negative (ER + /HER2-)) BC remains unclear. In this study, we used artificial intelligence (AI) to assess the prognostic significance of TILs in a large well-characterised cohort of luminal BC. METHODS: Supervised deep learning model analysis of Haematoxylin and Eosin (H&E)-stained whole slide images (WSI) was applied to a cohort of 2231 luminal early-stage BC patients with long-term follow-up. Stromal TILs (sTILs) and intratumoural TILs (tTILs) were quantified and their spatial distribution within tumour tissue, as well as the proportion of stroma involved by sTILs were assessed. The association of TILs with clinicopathological parameters and patient outcome was determined. RESULTS: A strong positive linear correlation was observed between sTILs and tTILs. High sTILs and tTILs counts, as well as their proximity to stromal and tumour cells (co-occurrence) were associated with poor clinical outcomes and unfavourable clinicopathological parameters including high tumour grade, lymph node metastasis, large tumour size, and young age. AI-based assessment of the proportion of stroma composed of sTILs (as assessed visually in routine practice) was not predictive of patient outcome. tTILs was an independent predictor of worse patient outcome in multivariate Cox Regression analysis. CONCLUSION: AI-based detection of TILs counts, and their spatial distribution provides prognostic value in luminal early-stage BC patients. The utilisation of AI algorithms could provide a comprehensive assessment of TILs as a morphological variable in WSIs beyond eyeballing assessment.
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spelling pubmed-106675372023-09-30 Evaluation of tumour infiltrating lymphocytes in luminal breast cancer using artificial intelligence Makhlouf, Shorouk Wahab, Noorul Toss, Michael Ibrahim, Asmaa Lashen, Ayat G. Atallah, Nehal M. Ghannam, Suzan Jahanifar, Mostafa Lu, Wenqi Graham, Simon Mongan, Nigel P. Bilal, Mohsin Bhalerao, Abhir Snead, David Minhas, Fayyaz Raza, Shan E. Ahmed Rajpoot, Nasir Rakha, Emad Br J Cancer Article BACKGROUND: Tumour infiltrating lymphocytes (TILs) are a prognostic parameter in triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC). However, their role in luminal (oestrogen receptor positive and HER2 negative (ER + /HER2-)) BC remains unclear. In this study, we used artificial intelligence (AI) to assess the prognostic significance of TILs in a large well-characterised cohort of luminal BC. METHODS: Supervised deep learning model analysis of Haematoxylin and Eosin (H&E)-stained whole slide images (WSI) was applied to a cohort of 2231 luminal early-stage BC patients with long-term follow-up. Stromal TILs (sTILs) and intratumoural TILs (tTILs) were quantified and their spatial distribution within tumour tissue, as well as the proportion of stroma involved by sTILs were assessed. The association of TILs with clinicopathological parameters and patient outcome was determined. RESULTS: A strong positive linear correlation was observed between sTILs and tTILs. High sTILs and tTILs counts, as well as their proximity to stromal and tumour cells (co-occurrence) were associated with poor clinical outcomes and unfavourable clinicopathological parameters including high tumour grade, lymph node metastasis, large tumour size, and young age. AI-based assessment of the proportion of stroma composed of sTILs (as assessed visually in routine practice) was not predictive of patient outcome. tTILs was an independent predictor of worse patient outcome in multivariate Cox Regression analysis. CONCLUSION: AI-based detection of TILs counts, and their spatial distribution provides prognostic value in luminal early-stage BC patients. The utilisation of AI algorithms could provide a comprehensive assessment of TILs as a morphological variable in WSIs beyond eyeballing assessment. Nature Publishing Group UK 2023-09-30 2023-11-23 /pmc/articles/PMC10667537/ /pubmed/37777578 http://dx.doi.org/10.1038/s41416-023-02451-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access 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/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Makhlouf, Shorouk
Wahab, Noorul
Toss, Michael
Ibrahim, Asmaa
Lashen, Ayat G.
Atallah, Nehal M.
Ghannam, Suzan
Jahanifar, Mostafa
Lu, Wenqi
Graham, Simon
Mongan, Nigel P.
Bilal, Mohsin
Bhalerao, Abhir
Snead, David
Minhas, Fayyaz
Raza, Shan E. Ahmed
Rajpoot, Nasir
Rakha, Emad
Evaluation of tumour infiltrating lymphocytes in luminal breast cancer using artificial intelligence
title Evaluation of tumour infiltrating lymphocytes in luminal breast cancer using artificial intelligence
title_full Evaluation of tumour infiltrating lymphocytes in luminal breast cancer using artificial intelligence
title_fullStr Evaluation of tumour infiltrating lymphocytes in luminal breast cancer using artificial intelligence
title_full_unstemmed Evaluation of tumour infiltrating lymphocytes in luminal breast cancer using artificial intelligence
title_short Evaluation of tumour infiltrating lymphocytes in luminal breast cancer using artificial intelligence
title_sort evaluation of tumour infiltrating lymphocytes in luminal breast cancer using artificial intelligence
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10667537/
https://www.ncbi.nlm.nih.gov/pubmed/37777578
http://dx.doi.org/10.1038/s41416-023-02451-3
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