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A digital score of tumour‐associated stroma infiltrating lymphocytes predicts survival in head and neck squamous cell carcinoma

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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.2017.14585openalex-snapshot
Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer
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Domain-Adversarial Neural Networks to Address the Appearance Variability of Histopathology Images
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Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer
inferredcites →https://doi.org/10.1016/j.celrep.2018.03.086openalex-snapshot
Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images
inferredcites →https://doi.org/10.1016/j.immuni.2018.03.023openalex-snapshot
The Immune Landscape of Cancer
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Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features
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Feature-driven local cell graph (FLocK): New computational pathology-based descriptors for prognosis of lung cancer and HPV status of oropharyngeal cancers
inferredcites →https://doi.org/10.1016/j.tranon.2016.10.005openalex-snapshot
Prognostic Value of Tumor-Infiltrating Lymphocytes for Patients With Head and Neck Squamous Cell Carcinoma
inferredcites →https://doi.org/10.1016/s2589-7500(20)30159-xopenalex-snapshot
An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study
inferredcites →https://doi.org/10.1017/s0022215116000360openalex-snapshot
Aetiology and risk factors for head and neck cancer: United Kingdom National Multidisciplinary Guidelines
inferredcites →https://doi.org/10.1038/bjc.2013.639openalex-snapshot
Tumour-infiltrating lymphocytes predict for outcome in HPV-positive oropharyngeal cancer
inferredcites →https://doi.org/10.1038/bjc.2013.640openalex-snapshot
Tumour-infiltrating lymphocytes predict response to definitive chemoradiotherapy in head and neck cancer
inferredcites →https://doi.org/10.1038/labinvest.2017.131openalex-snapshot
Integrated tumor identification and automated scoring minimizes pathologist involvement and provides new insights to key biomarkers in breast cancer
inferredcites →https://doi.org/10.1038/modpathol.2015.133openalex-snapshot
Erratum: Beyond immune density: critical role of spatial heterogeneity in estrogen receptor-negative breast cancer
inferredcites →https://doi.org/10.1038/modpathol.2015.37openalex-snapshot
Beyond immune density: critical role of spatial heterogeneity in estrogen receptor-negative breast cancer
inferredcites →https://doi.org/10.1038/s41416-018-0202-yopenalex-snapshot
Tumour-stroma ratio and prognosis in gastric adenocarcinoma
inferredcites →https://doi.org/10.1038/s41551-020-00682-wopenalex-snapshot
Data-efficient and weakly supervised computational pathology on whole-slide images
inferredcites →https://doi.org/10.1038/s41591-019-0508-1openalex-snapshot
Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
inferredcites →https://doi.org/10.1038/s41591-020-0900-xopenalex-snapshot
Geospatial immune variability illuminates differential evolution of lung adenocarcinoma
inferredcites →https://doi.org/10.1038/s41598-019-49710-zopenalex-snapshot
A Novel Digital Score for Abundance of Tumour Infiltrating Lymphocytes Predicts Disease Free Survival in Oral Squamous Cell Carcinoma
inferredcites →https://doi.org/10.1080/2162402x.2017.1356148openalex-snapshot
The prognostic role of tumor infiltrating T-lymphocytes in squamous cell carcinoma of the head and neck: A systematic review and meta-analysis
inferredcites →https://doi.org/10.1093/annonc/mdu450openalex-snapshot
The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: recommendations by an International TILs Working Group 2014
inferredcites →https://doi.org/10.1109/tmi.2019.2891305openalex-snapshot
Fast ScanNet: Fast and Dense Analysis of Multi-Gigapixel Whole-Slide Images for Cancer Metastasis Detection
inferredcites →https://doi.org/10.1109/tmi.2020.2971006openalex-snapshot
Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images
inferredcites →https://doi.org/10.1109/tmi.2021.3056023openalex-snapshot
Self-Path: Self-Supervision for Classification of Pathology Images With Limited Annotations
inferredcites →https://doi.org/10.1111/jop.12698openalex-snapshot
Density and location of CD 3+ and CD 8+ tumor‐infiltrating lymphocytes correlate with prognosis of oral squamous cell carcinoma
inferredcites →https://doi.org/10.1158/0008-5472.can-16-0633openalex-snapshot
HPV-Related Oropharynx Cancer in the United Kingdom: An Evolution in the Understanding of Disease Etiology
inferredcites →https://doi.org/10.1158/1078-0432.ccr-18-3803openalex-snapshot
Tumor-Infiltrating Lymphocytes—Location for Prognostic Evaluation
inferredcites →https://doi.org/10.1186/s12885-017-3317-2openalex-snapshot
Prognostic significance of tumor infiltrating immune cells in oral squamous cell carcinoma
inferredcites →https://doi.org/10.1186/s13058-015-0638-4openalex-snapshot
An ecological measure of immune-cancer colocalization as a prognostic factor for breast cancer
inferredcites →https://doi.org/10.1634/theoncologist.10-7-493openalex-snapshot
Second Field Tumors: A New Opportunity for Cancer Prevention?
inferredcites →https://doi.org/10.3322/caac.21492openalex-snapshot
Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
inferredcites →https://doi.org/10.3322/canjclin.57.1.43openalex-snapshot
Cancer Statistics, 2007

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Artificial intelligence to identify genetic alterations in conventional histopathology
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Computational pathology in cancer diagnosis, prognosis, and prediction – present day and prospects
inferred← citeshttps://doi.org/10.1007/978-3-031-12053-4_32openalex-snapshot
A Novel Framework for Coarse-Grained Semantic Segmentation of Whole-Slide Images
inferred← citeshttps://doi.org/10.1007/s12672-024-00870-zopenalex-snapshot
Exploring the frontiers: tumor immune microenvironment and immunotherapy in head and neck squamous cell carcinoma
inferred← citeshttps://doi.org/10.1016/b978-0-323-95223-1.00016-3openalex-snapshot
Digital pathology and artificial intelligence in cancer research
inferred← citeshttps://doi.org/10.1016/j.biopha.2024.117544openalex-snapshot
The prognostic value of tumor-infiltrating lymphocytes in head and neck squamous cell carcinoma: A systematic review and meta-analysis
inferred← citeshttps://doi.org/10.1016/j.cden.2025.11.009openalex-snapshot
Artificial Intelligence and Its Applications in Oral and Maxillofacial Pathology
inferred← citeshttps://doi.org/10.1016/j.eswa.2024.124242openalex-snapshot
A pathology-based diagnosis and prognosis intelligent system for oral squamous cell carcinoma using semi-supervised learning
inferred← citeshttps://doi.org/10.1016/j.heliyon.2023.e14371openalex-snapshot
A pathomic approach for tumor-infiltrating lymphocytes classification on breast cancer digital pathology images
inferred← citeshttps://doi.org/10.1016/j.jss.2024.01.043openalex-snapshot
Immune Landscape of Epstein–Barr Virus–Associated Gastric Cancer: Analysis From a Western Academic Institution
inferred← citeshttps://doi.org/10.1016/j.media.2023.102997openalex-snapshot
Consistency regularisation in varying contexts and feature perturbations for semi-supervised semantic segmentation of histology images
inferred← citeshttps://doi.org/10.1016/j.media.2023.103070openalex-snapshot
Attention De-sparsification Matters: Inducing diversity in digital pathology representation learning
inferred← citeshttps://doi.org/10.1016/j.modpat.2023.100320openalex-snapshot
A Novel Deep Learning Algorithm for Human Papillomavirus Infection Prediction in Head and Neck Cancers Using Routine Histology Images
inferred← citeshttps://doi.org/10.1016/j.oooo.2026.03.006openalex-snapshot
Deep learning approach to identify histological features associated with lymph node metastasis following primary tumor excision in patients with tongue squamous cell carcinoma
inferred← citeshttps://doi.org/10.1016/j.oraloncology.2024.106750openalex-snapshot
An algorithm for standardization of tumor Infiltrating lymphocyte evaluation in head and neck cancers
inferred← citeshttps://doi.org/10.1016/j.oraloncology.2024.106829openalex-snapshot
Assessment of tumor-infiltrating lymphocytes in head and neck cancer: Clinical scenarios
inferred← citeshttps://doi.org/10.1016/j.prp.2023.154342openalex-snapshot
Clinical significance of overall assessment of tumor-infiltrating lymphocytes in oropharyngeal cancer: A meta-analysis
inferred← citeshttps://doi.org/10.1038/s41467-025-57717-6openalex-snapshot
Histopathology based AI model predicts anti-angiogenic therapy response in renal cancer clinical trial
inferred← citeshttps://doi.org/10.1038/s41598-023-40581-zopenalex-snapshot
Lymphocyte detection for cancer analysis using a novel fusion block based channel boosted CNN
inferred← citeshttps://doi.org/10.1038/s41598-025-86527-5openalex-snapshot
Classifying tumour infiltrating lymphocytes in oral squamous cell carcinoma histopathology using joint learning framework
inferred← citeshttps://doi.org/10.1038/s41598-026-44633-yopenalex-snapshot
Estimating major pathological response in non-small cell lung cancer patients with post-neoadjuvant therapy using MMT-net
inferred← citeshttps://doi.org/10.1038/s41698-025-00866-0openalex-snapshot
Prediction and analysis of tumor infiltrating lymphocytes across 28 cancers by TILScout using deep learning
inferred← citeshttps://doi.org/10.1038/s44172-024-00246-9openalex-snapshot
Annotation-efficient deep learning for breast cancer whole-slide image classification using tumour infiltrating lymphocytes and slide-level labels
inferred← citeshttps://doi.org/10.1080/1744666x.2023.2262140openalex-snapshot
Immune-scoring in head and neck squamous cell carcinoma: a scoping review
inferred← citeshttps://doi.org/10.1101/2025.02.28.25323078openalex-snapshot
Tumor-infiltrating lymphocytes in breast cancer through artificial intelligence: biomarker analysis from the results of the TIGER challenge
inferred← citeshttps://doi.org/10.1111/jop.70002openalex-snapshot
Exploring the Role of Artificial Intelligence in Oral Pathology: Diagnostic and Prognostic Implications
inferred← citeshttps://doi.org/10.1136/jitc-2025-014429openalex-snapshot
Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application
inferred← citeshttps://doi.org/10.1155/2023/4275998openalex-snapshot
Systemic Immune-Inflammatory Index, Tumor-Infiltrating Lymphocytes, and Clinical Outcomes in Esophageal Squamous Cell Carcinoma Receiving Concurrent Chemoradiotherapy
inferred← citeshttps://doi.org/10.1155/2023/7717712openalex-snapshot
Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network
inferred← citeshttps://doi.org/10.1186/s12885-025-15421-0openalex-snapshot
Supervised machine learning to quantitatively assess the prognostic value of tumor-infiltrating lymphocytes in gastric cancer
inferred← citeshttps://doi.org/10.1200/po.24.00183openalex-snapshot
CD8 + Tumor-Infiltrating Lymphocytes in Head and Neck Cancer: A Review
inferred← citeshttps://doi.org/10.17305/bb.2024.10974openalex-snapshot
Detecting cell types and densities in the tumor microenvironment improves prognostic risk assessment for breast cancer
inferred← citeshttps://doi.org/10.2478/abm-2023-0047openalex-snapshot
Acidic environment could modulate the interferon-γ expression: Implication on modulation of cancer and immune cells’ interactions
inferred← citeshttps://doi.org/10.3390/app12073273openalex-snapshot
TTCNN: A Breast Cancer Detection and Classification towards Computer-Aided Diagnosis Using Digital Mammography in Early Stages
inferred← citeshttps://doi.org/10.3390/cancers14061558openalex-snapshot
Tumor-Infiltrating Lymphocytes in Head and Neck Cancer: Ready for Prime Time?
inferred← citeshttps://doi.org/10.3390/cancers15153796openalex-snapshot
Modelling the Tumour Microenvironment, but What Exactly Do We Mean by “Model”?
inferred← citeshttps://doi.org/10.3390/diagnostics13142416openalex-snapshot
Artificial Intelligence for Image Analysis in Oral Squamous Cell Carcinoma: A Review
inferred← citeshttps://doi.org/10.3390/jpm13050726openalex-snapshot
The Prognostic Role of the Immune Microenvironment in Sinonasal Intestinal-Type Adenocarcinoma: A Computer-Assisted Image Analysis of CD3+ and CD8+ Tumor-Infiltrating Lymphocytes
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