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Multiscale deep learning framework captures systemic immune features in lymph nodes predictive of triple negative breast cancer outcome in large‐scale studies
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1001/jama.2017.11470openalex-snapshot
Effect of Axillary Dissection vs No Axillary Dissection on 10-Year Overall Survival Among Women With Invasive Breast Cancer and Sentinel Node Metastasis
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High-Dose Chemotherapy With Hematopoietic Stem Cell Transplant in Patients With High-Risk Breast Cancer and 4 or More Involved Axillary Lymph Nodes
inferredcites →https://doi.org/10.1002/cjp2.87openalex-snapshot
Histological scoring of immune and stromal features in breast and axillary lymph nodes is prognostic for distant metastasis in lymph node‐positive breast cancers
inferredcites →https://doi.org/10.1007/978-3-319-24574-4_28openalex-snapshot
U-Net: Convolutional Networks for Biomedical Image Segmentation
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Detection of Lung Cancer Lymph Node Metastases from Whole-Slide Histopathologic Images Using a Two-Step Deep Learning Approach
inferredcites →https://doi.org/10.1016/j.cell.2012.07.021openalex-snapshot
A Spatially-Organized Multicellular Innate Immune Response in Lymph Nodes Limits Systemic Pathogen Spread
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A Computational Tumor-Infiltrating Lymphocyte Assessment Method Comparable with Visual Reporting Guidelines for Triple-Negative Breast Cancer
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What do we still need to learn on digitally assessed biomarkers?
inferredcites →https://doi.org/10.1016/j.ejca.2021.01.037openalex-snapshot
Immune analysis of lymph nodes in relation to the presence or absence of tumor infiltrating lymphocytes in triple-negative breast cancer
inferredcites →https://doi.org/10.1016/s1470-2045(18)30380-2openalex-snapshot
Axillary dissection versus no axillary dissection in patients with breast cancer and sentinel-node micrometastases (IBCSG 23-01): 10-year follow-up of a randomised, controlled phase 3 trial
inferredcites →https://doi.org/10.1038/nri2454openalex-snapshot
The who, how and where of antigen presentation to B cells
inferredcites →https://doi.org/10.1038/s41523-021-00292-yopenalex-snapshot
Systemic immune reaction in axillary lymph nodes adds to tumor-infiltrating lymphocytes in triple-negative breast cancer prognostication
inferredcites →https://doi.org/10.1038/s41568-020-00327-9openalex-snapshot
Designing deep learning studies in cancer diagnostics
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Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology
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Swarm Learning for decentralized and confidential clinical machine learning
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Deep learning in histopathology: the path to the clinic
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QuPath: Open source software for digital pathology image analysis
inferredcites →https://doi.org/10.1038/s41598-018-23629-3openalex-snapshot
Morphology of Immunomodulation in Breast Cancer Tumor Draining Lymph Nodes Depends on Stage and Intrinsic Subtype
inferredcites →https://doi.org/10.1038/s41598-021-90821-3openalex-snapshot
Learning from crowds in digital pathology using scalable variational Gaussian processes
inferredcites →https://doi.org/10.1038/s43018-022-00436-4openalex-snapshot
Artificial intelligence in histopathology: enhancing cancer research and clinical oncology
inferredcites →https://doi.org/10.1038/sj.bjc.6602678openalex-snapshot
REporting recommendations for tumour MARKer prognostic studies (REMARK)
inferredcites →https://doi.org/10.1056/nejmoa1910549openalex-snapshot
Pembrolizumab for Early Triple-Negative Breast Cancer
inferredcites →https://doi.org/10.1093/annonc/mdv106openalex-snapshot
Guidelines for time-to-event end point definitions in breast cancer trials: results of the DATECAN initiative (Definition for the Assessment of Time-to-event Endpoints in CANcer trials)
inferredcites →https://doi.org/10.1093/annonc/mdv478openalex-snapshot
Guidelines for time-to-event end point definitions in breast cancer trials: results of the DATECAN initiative (Definition for the Assessment of Time-to-event Endpoints in CANcer trials)(vol 26, pg 873, 2015)
inferredcites →https://doi.org/10.1093/gigascience/giy065openalex-snapshot
1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset
inferredcites →https://doi.org/10.1109/bibe55377.2022.00027openalex-snapshot
Deep Multi-Scale U-Net Architecture and Label-Noise Robust Training Strategies for Histopathological Image Segmentation
inferredcites →https://doi.org/10.1109/tpami.2016.2572683openalex-snapshot
Fully Convolutional Networks for Semantic Segmentation
inferredcites →https://doi.org/10.1109/tsmc.1979.4310076openalex-snapshot
A Threshold Selection Method from Gray-Level Histograms
inferredcites →https://doi.org/10.1111/cas.13137openalex-snapshot
Possible functions of CD 169‐positive sinus macrophages in lymph nodes in anti‐tumor immune responses
inferredcites →https://doi.org/10.1126/science.aaf1328openalex-snapshot
SCS macrophages suppress melanoma by restricting tumor-derived vesicle–B cell interactions
inferredcites →https://doi.org/10.1126/sciimmunol.abg3551openalex-snapshot
Tumor-draining lymph nodes: At the crossroads of metastasis and immunity
inferredcites →https://doi.org/10.1186/bcr3655openalex-snapshot
Genomic patterns resembling BRCA1- and BRCA2-mutated breast cancers predict benefit of intensified carboplatin-based chemotherapy
inferredcites →https://doi.org/10.1293/tox.25.63openalex-snapshot
Histopathology of Incidental Findings in Cynomolgus Monkeys (Macaca Fascicularis) Used in Toxicity Studies
inferredcites →https://doi.org/10.32614/r.manualsopenalex-snapshot
R: A Language and Environment for Statistical Computing
inferredcites →https://doi.org/10.3389/fcomp.2021.684521openalex-snapshot
Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images
inferredcites →https://doi.org/10.3389/fgene.2021.639930openalex-snapshot
MSU-Net: Multi-Scale U-Net for 2D Medical Image Segmentation
inferredcites →https://doi.org/10.3389/fmolb.2021.673051openalex-snapshot
Immune Crosstalk Between Lymph Nodes and Breast Carcinomas, With a Focus on B Cells
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/cai2.159openalex-snapshot
Artificial Intelligence and Breast Cancer Management: From Data to the Clinic
inferred← citeshttps://doi.org/10.1002/path.6163openalex-snapshot
Computational pathology in cancer diagnosis, prognosis, and prediction – present day and prospects
inferred← citeshttps://doi.org/10.1002/path.6165openalex-snapshot
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
inferred← citeshttps://doi.org/10.1007/978-3-031-52860-6_10openalex-snapshot
Revolutionizing Breast Cancer Treatment: Harnessing the Power of Artificial Intelligence in Overcoming Drug Resistance
inferred← citeshttps://doi.org/10.1007/978-3-031-98694-9_7openalex-snapshot
A Hybrid Transformer-Graph Model for Multi-Class Lymph Node Segmentation in Histopathology
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ncRNAs and their impact on dopaminergic neurons: Autophagy pathways in Parkinson's disease
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Histiocyte Quantity in Tumor-Negative Sentinel Lymph Nodes Predicts 10-Year Disease-Free Survival in Patients With Breast Cancer
inferred← citeshttps://doi.org/10.1016/j.ejca.2026.116910openalex-snapshot
The clinical relevance of regional lymph node microarchitecture in oesophageal cancer patients – Results from the UK MRC OE02 trial
inferred← citeshttps://doi.org/10.1016/j.isci.2025.112765openalex-snapshot
Entropy measures for quantifying complexity in digital pathology and spatial omics
inferred← citeshttps://doi.org/10.1016/j.jncc.2025.11.002openalex-snapshot
MCLAM: a cost-effective deep learning model for predicting recurrence risk in HR+/HER2− breast cancer—a multi-center study in a Chinese cohort
inferred← citeshttps://doi.org/10.1016/j.jpi.2024.100367openalex-snapshot
Computational methods for metastasis detection in lymph nodes and characterization of the metastasis-free lymph node microarchitecture: A systematic-narrative hybrid review
inferred← citeshttps://doi.org/10.1016/j.modpat.2025.100948openalex-snapshot
Quantitative Histology of Nonmetastatic Regional Lymph Nodes as a Novel Prognostic Indicator in Microsatellite Instability–High Colorectal Cancer
inferred← citeshttps://doi.org/10.1016/j.modpat.2026.101003openalex-snapshot
MetAssist 2.0: A Generalizable Artificial Intelligence Framework for Lymph Node Metastasis Detection Across Multiple Cancer Types
inferred← citeshttps://doi.org/10.1016/j.xpro.2025.103624openalex-snapshot
Protocol for perfusing human axillary lymph nodes ex vivo to study structure and function in real time
inferred← citeshttps://doi.org/10.1038/s41598-024-81383-1openalex-snapshot
Deciphering cell to cell spatial relationship for pathology images using SpatialQPFs
inferred← citeshttps://doi.org/10.1109/isbi60581.2025.10981028openalex-snapshot
Pathogen-X: A Cross-Modal Genomic Feature Trans-Align Network for Enhanced Survival Prediction from Histopathology Images
inferred← citeshttps://doi.org/10.1111/his.70149openalex-snapshot
World Health Organization classification of tumours of the breast 6th edition 2026
inferred← citeshttps://doi.org/10.1159/000543600openalex-snapshot
Prognostic Impact of Immunophenotypic Variation in Subcapsular Sinus Endothelium of Sentinel Lymph Nodes in Invasive Breast Carcinoma
inferred← citeshttps://doi.org/10.1186/s12885-024-12962-8openalex-snapshot
Automated scoring methods for quantitative interpretation of Tumour infiltrating lymphocytes (TILs) in breast cancer: a systematic review
inferred← citeshttps://doi.org/10.1186/s13045-023-01514-5openalex-snapshot
Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment
inferred← citeshttps://doi.org/10.1186/s13058-024-01848-zopenalex-snapshot
Pre-treatment peripheral blood immunophenotyping and response to neoadjuvant chemotherapy in operable breast cancer
inferred← citeshttps://doi.org/10.1186/s13058-024-01895-6openalex-snapshot
Deep learning applications in breast cancer histopathological imaging: diagnosis, treatment, and prognosis
inferred← citeshttps://doi.org/10.3390/biomedinformatics5010009openalex-snapshot
A Decision-Aid Model for Predicting Triple-Negative Breast Cancer ICI Response Based on Tumor Mutation Burden
inferred← citeshttps://doi.org/10.3390/cancers18071184openalex-snapshot
Integrating Artificial Intelligence into Breast Cancer Histopathology: Toward Improved Diagnosis and Prognosis
inferred← citeshttps://doi.org/10.6084/m9.figshare.26732029openalex-snapshot
Additional file 1 of Pre-treatment peripheral blood immunophenotyping and response to neoadjuvant chemotherapy in operable breast cancer
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