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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| Title: | 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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| Authors: | Albusayli R; Tissue Image Analytics Centre, The University of Warwick, Coventry, UK., Graham JD; The Westmead Institute for Medical Research, The University of Sydney, Sydney, NSW, Australia.; Westmead Breast Cancer Institute, Western Sydney Local Health District, Sydney, NSW, Australia., Pathmanathan N; Westmead Breast Cancer Institute, Western Sydney Local Health District, Sydney, NSW, Australia.; Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia., Shaban M; Harvard Medical School, Harvard University, Boston, MA, USA., Raza SEA; Tissue Image Analytics Centre, The University of Warwick, Coventry, UK., Minhas F; Tissue Image Analytics Centre, The University of Warwick, Coventry, UK., Armes JE; Pathology Queensland, Queensland Health, Herston, QLD, Australia., Rajpoot N; Tissue Image Analytics Centre, The University of Warwick, Coventry, UK.; The Alan Turing Institute, London, UK. |
| Source: | The Journal of pathology [J Pathol] 2023 May; Vol. 260 (1), pp. 32-42. Date of Electronic Publication: 2023 Feb 24. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: John Wiley And Sons Country of Publication: England NLM ID: 0204634 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1096-9896 (Electronic) Linking ISSN: 00223417 NLM ISO Abbreviation: J Pathol Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1096-9896 |
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| DOI: | 10.1002/path.6061 |
