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Artificial intelligence‐based digital scores of stromal tumour‐infiltrating lymphocytes and tumour‐associated stroma predict disease‐specific survival in triple‐negative breast cancer
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Albusayli, Rawan, Graham, J. Dinny, Pathmanathan, Nirmala, Shaban, Muhammad, Raza, Shan E. Ahmed, Minhas, Fayyaz ul Amir Afsar, Armes, Jane E. and Rajpoot, Nasir M. (Nasir Mahmood) (2023) Artificial intelligence‐based digital scores of stromal tumour‐infiltrating lymphocytes and tumour‐associated stroma predict disease‐specific survival in triple‐negative breast cancer. The Journal of Pathology . doi:10.1002/path.6061 ISSN 0022-3417. (In Press)
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The Journal of Pathology - 2023 - Albusayli - Artificial intelligence‐based digital scores of stromal tumour‐infiltrating.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (22Mb) | Preview |
Official URL: http://doi.org/10.1002/path.6061
Abstract
Triple-negative breast cancer (TNBC) is known to have a relatively poor outcome with variable prognoses, raising the need for more informative risk stratification. We investigated a set of digital, artificial intelligence (AI)-based spatial tumour microenvironment (sTME) features and explored their prognostic value in TNBC. After performing tissue classification on digitised haematoxylin and eosin (H&E) slides of TNBC cases, we employed a deep learning-based algorithm to segment tissue regions into tumour, stroma, and lymphocytes in order to compute quantitative features concerning the spatial relationship of tumour with lymphocytes and stroma. The prognostic value of the digital features was explored using survival analysis with Cox proportional hazard models in a cross-validation setting on two independent international multi-centric TNBC cohorts: The Australian Breast Cancer Tissue Bank (AUBC) cohort (n = 318) and The Cancer Genome Atlas Breast Cancer (TCGA) cohort (n = 111). The proposed digital stromal tumour-infiltrating lymphocytes (Digi-sTILs) score and the digital tumour-associated stroma (Digi-TAS) score were found to carry strong prognostic value for disease-specific survival, with the Digi-sTILs and Digi-TAS scores giving C-index values of 0.65 (p = 0.0189) and 0.60 (p = 0.0437), respectively, on the TCGA cohort as a validation set. Combining the Digi-sTILs feature with the patient's positivity status for axillary lymph nodes yielded a C-index of 0.76 on unseen validation cohorts. We surmise that the proposed digital features could potentially be used for better risk stratification and management of TNBC patients. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
| Item Type: | Journal Article | ||||||||
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||
| Journal or Publication Title: | The Journal of Pathology | ||||||||
| Publisher: | Wiley | ||||||||
| ISSN: | 0022-3417 | ||||||||
| Official Date: | 2023 | ||||||||
| Dates: |
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| DOI: | 10.1002/path.6061 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | In Press | ||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||
| Date of first compliant deposit: | 20 March 2023 | ||||||||
| Date of first compliant Open Access: | 20 March 2023 |
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