ArticleBritish journal of cancer2023
Evaluation of tumour infiltrating lymphocytes in luminal breast cancer using artificial intelligence.
Article in British journal of cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Automated scoring methods for quantitative interpretation of Tumour infiltrating lymphocytes (TILs) in breast cancer: a systematic review.BMC cancer · 2024Pooled it
- Thematic trends and knowledge-map of tumor-infiltrating lymphocytes in breast cancer: a scientometric analysis.Frontiers in oncology · 2024Pooled it
- LC-MS/MS-based clinical value exploration of the methionine cycle in luminal breast cancer.Oncology letters · 2026Article
- Amide proton transfer-weighted magnetic resonance imaging for the diagnosis of triple-negative breast cancer: a comparative study with diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2026Article
- Tumour-infiltrating lymphocytes differ across MammaPrint® classifications in breast cancer.Endocrine-related cancer · 2026Article
- AI-assisted image analysis of tumor-infiltrating lymphocytes as a prognostic marker in chemotherapy-naïve luminal breast cancer.NPJ breast cancer · 2026Article
- QuTILs: Open-Source Image-Based Infiltrating Immune Cell Detection for Research Application.Research square · 2026Article
- AI-powered and manual assessment of tumor-infiltrating lymphocytes in early HER2-positive breast cancer in NSABP B-41.NPJ breast cancer · 2026Article
- E-cadherin inactivation shapes tumor microenvironment specificities in invasive lobular breast cancer.Nature communications · 2026Article
- Integrating Artificial Intelligence into Breast Cancer Histopathology: Toward Improved Diagnosis and Prognosis.Cancers · 2026Review
- Multimodal radiomics for early prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer: integration of amide proton transfer weighted imaging in radiomics.BMC medical imaging · 2026Article
- Development of an artificial intelligence-based computer-aided detection system for routine gastric biopsy diagnosis.Journal of pathology informatics · 2026Article
- Real-world second- and third-line progression-free survival after progression on first-line CDK4/6 inhibitors in HR+/HER2- metastatic breast cancer by PAM50 intrinsic subtype: the SOLTI-1801 CDK-PREDICT study.Breast cancer research and treatment · 2026Observational
- TILseg: Automated Whole Slide-Level Spatial Scoring of Tumor-Infiltrating Lymphocytes Reveals Prognostic Patterns in Triple Negative Breast Cancer.medRxiv : the preprint server for health sciences · 2026Article
- A computer vision method to evaluate tumor-infiltrating lymphocytes and multiparametric modeling of neoadjuvant systemic therapy response in breast cancer.Therapeutic advances in medical oncology · 2026Article
- Article
- AUPA: weakly supervised approach for streamlining breast cancer diagnostic workflow by WSI histological type classification for efficient IHC triage.Scientific reports · 2025Article
- AI assessment of tumor-infiltrating lymphocytes on routine H&E-slides as a predictor of response to neoadjuvant therapy in breast cancer-a real-world study.Virchows Archiv : an international journal of pathology · 2025Article
- Assessment of Tumor-Infiltrating Lymphocytes in Triple-Negative Breast Cancer: Interobserver Variability and Contributing Factors.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial Intelligence-Based Pathology to Assist Prediction of Neoadjuvant Therapy Responses for Breast Cancer.Cancer medicine · 2025Review
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18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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