ArticleThe Journal of pathology2023
Multiscale deep learning framework captures systemic immune features in lymph nodes predictive of triple negative breast cancer outcome in large-scale studies.
Article in The Journal of pathology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Automated scoring methods for quantitative interpretation of Tumour infiltrating lymphocytes (TILs) in breast cancer: a systematic review.BMC cancer · 2024Pooled it
- World Health Organization classification of tumours of the breast 6th edition 2026.Histopathology · 2026Review
- MCLAM: a cost-effective deep learning model for predicting recurrence risk in HR+/HER2- breast cancer-a multi-center study in a Chinese cohort.Journal of the National Cancer Center · 2026Article
- Integrating Artificial Intelligence into Breast Cancer Histopathology: Toward Improved Diagnosis and Prognosis.Cancers · 2026Review
- Review
- Artificial Intelligence and Breast Cancer Management: From Data to the Clinic.Cancer innovation · 2025Review
- Prognostic Impact of Immunophenotypic Variation in Subcapsular Sinus Endothelium of Sentinel Lymph Nodes in Invasive Breast Carcinoma.Breast care (Basel, Switzerland) · 2025Article
- Deep learning-based multi-task prediction of response to neoadjuvant chemotherapy using multiscale whole slide images in breast cancer: A multicenter study.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2025Article
- Computational methods for metastasis detection in lymph nodes and characterization of the metastasis-free lymph node microarchitecture: A systematic-narrative hybrid review.Journal of pathology informatics · 2024Review
- Deciphering cell to cell spatial relationship for pathology images using SpatialQPFs.Scientific reports · 2024Article
- Deep learning applications in breast cancer histopathological imaging: diagnosis, treatment, and prognosis.Breast cancer research : BCR · 2024Review
- Pre-treatment peripheral blood immunophenotyping and response to neoadjuvant chemotherapy in operable breast cancer.Breast cancer research : BCR · 2024Article
- Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment.Journal of hematology & oncology · 2023Review
- Computational pathology in cancer diagnosis, prognosis, and prediction - present day and prospects.The Journal of pathology · 2023Review
- Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer.The Journal of pathology · 2023Review
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23 authors at 7 institutions in 7 countries.
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Abstract
The suggestion that the systemic immune response in lymph nodes (LNs) conveys prognostic value for triple-negative breast cancer (TNBC) patients has not previously been investigated in large cohorts. We used a deep learning (DL) framework to quantify morphological features in haematoxylin and eosin-stained LNs on digitised whole slide images. From 345 breast cancer patients, 5,228 axillary LNs, cancer-free and involved, were assessed. Generalisable multiscale DL frameworks were developed to capture and quantify germinal centres (GCs) and sinuses. Cox regression proportional hazard models tested the association between smuLymphNet-captured GC and sinus quantifications and distant metastasis-free survival (DMFS). smuLymphNet achieved a Dice coefficient of 0.86 and 0.74 for capturing GCs and sinuses, respectively, and was comparable to an interpathologist Dice coefficient of 0.66 (GC) and 0.60 (sinus). smuLymphNet-captured sinuses were increased in LNs harbouring GCs (p < 0.001). smuLymphNet-captured GCs retained clinical relevance in LN-positive TNBC patients whose cancer-free LNs had on average ≥2 GCs, had longer DMFS (hazard ratio [HR] = 0.28, p = 0.02) and extended GCs' prognostic value to LN-negative TNBC patients (HR = 0.14, p = 0.002). Enlarged smuLymphNet-captured sinuses in involved LNs were associated with superior DMFS in LN-positive TNBC patients in a cohort from Guy's Hospital (multivariate HR = 0.39, p = 0.039) and with distant recurrence-free survival in 95 LN-positive TNBC patients of the Dutch-N4plus trial (HR = 0.44, p = 0.024). Heuristic scoring of subcapsular sinuses in LNs of LN-positive Tianjin TNBC patients (n = 85) cross-validated the association of enlarged sinuses with shorter DMFS (involved LNs: HR = 0.33, p = 0.029 and cancer-free LNs: HR = 0.21 p = 0.01). Morphological LN features reflective of cancer-associated responses are robustly quantifiable by smuLymphNet. Our findings further strengthen the value of assessment of LN properties beyond the detection of metastatic deposits for prognostication 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.
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