Artificial intelligence-based digital scores of stromal tumour-infiltrating lymphocytes and tumour-associated stroma predict disease-specific survival in triple-negative breast cancer

Rawan Albusayli; J Dinny Graham; Nirmala Pathmanathan; Muhammad Shaban; Shan E Ahmed Raza; Fayyaz Minhas; Jane E Armes; Nasir Rajpoot

doi:10.1002/path.6061

Artificial intelligence-based digital scores of stromal tumour-infiltrating lymphocytes and tumour-associated stroma predict disease-specific survival in triple-negative breast cancer

J Pathol. 2023 May;260(1):32-42. doi: 10.1002/path.6061. Epub 2023 Feb 24.

Authors

Rawan Albusayli¹, J Dinny Graham^{2

3}, Nirmala Pathmanathan^{3

4}, Muhammad Shaban⁵, Shan E Ahmed Raza¹, Fayyaz Minhas¹, Jane E Armes⁶, Nasir Rajpoot^{1

7}

Affiliations

¹ Tissue Image Analytics Centre, The University of Warwick, Coventry, UK.
² The Westmead Institute for Medical Research, The University of Sydney, Sydney, NSW, Australia.
³ Westmead Breast Cancer Institute, Western Sydney Local Health District, Sydney, NSW, Australia.
⁴ Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia.
⁵ Harvard Medical School, Harvard University, Boston, MA, USA.
⁶ Pathology Queensland, Queensland Health, Herston, QLD, Australia.
⁷ The Alan Turing Institute, London, UK.

PMID: 36705810
DOI: 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.

Keywords: artificial intelligence; disease-specific survival analysis; histology images; triple-negative breast cancer; tumour microenvironment.

MeSH terms

Artificial Intelligence
Australia
Humans
Lymphocytes, Tumor-Infiltrating / pathology
Prognosis
Triple Negative Breast Neoplasms* / genetics
Triple Negative Breast Neoplasms* / pathology
Tumor Microenvironment