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

被引:23
作者
Makhlouf, Shorouk [1 ,2 ]
Wahab, Noorul [3 ]
Toss, Michael [1 ,4 ]
Ibrahim, Asmaa [1 ,5 ]
Lashen, Ayat G. [1 ,6 ]
Atallah, Nehal M. [1 ,6 ]
Ghannam, Suzan [1 ,7 ]
Jahanifar, Mostafa [3 ]
Lu, Wenqi [3 ]
Graham, Simon [3 ]
Mongan, Nigel P. [8 ,9 ]
Bilal, Mohsin [3 ]
Bhalerao, Abhir [3 ]
Snead, David [10 ]
Minhas, Fayyaz [3 ]
Raza, Shan E. Ahmed [3 ]
Rajpoot, Nasir [3 ]
Rakha, Emad [1 ,11 ,12 ]
机构
[1] Univ Nottingham, Sch Med, Acad Unit Translat Med Sci, Nottingham, England
[2] Assiut Univ, Fac Med, Dept Pathol, Assiut, Egypt
[3] Univ Warwick, Tissue Image Analyt Ctr, Coventry, England
[4] Sheffield Univ Teaching Hosp NHS Trust, Dept Histopathol, Sheffield, England
[5] Suez Canal Univ, Fac Med, Dept Pathol, Ismailia, Egypt
[6] Menoufia Univ, Fac Med, Dept Pathol, Menoufia, Egypt
[7] Suez Canal Univ, Fac Med, Dept Histol & Cell Biol, Ismailia 41522, Egypt
[8] Univ Nottingham, Biodiscovery Inst, Sch Vet Med & Sci, Nottingham, England
[9] Weill Cornell Med, Dept Pharmacol, New York, NY 10065 USA
[10] Univ Hosp Coventry & Warwickshire, Coventry, England
[11] Nottingham Univ Hosp NHS Trust, Dept Histopathol, Nottingham, England
[12] Hamad Med Corp, Dept Pathol, Doha, Qatar
关键词
PROGNOSTIC VALUE; PREDICTIVE-VALUE; TILS; CHEMOTHERAPY; RECEPTOR; TRIAL;
D O I
10.1038/s41416-023-02451-3
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
BackgroundTumour 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.MethodsSupervised 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.ResultsA 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.ConclusionAI-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.
引用
收藏
页码:1747 / 1758
页数:12
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