ArticleJCO clinical cancer informatics2020
Deep-Learning-Based Characterization of Tumor-Infiltrating Lymphocytes in Breast Cancers From Histopathology Images and Multiomics Data.
Article in JCO clinical cancer informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.
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Who cites it
46 citing papers in PubMed, 1 synthesis or guideline pooled it, 94 citations in OpenAlex.
- Automated scoring methods for quantitative interpretation of Tumour infiltrating lymphocytes (TILs) in breast cancer: a systematic review.BMC cancer · 2024Pooled it
- BOMIFA: biologically informed multi-omics integration with graph contrastive learning for cancer prognosis in women.Briefings in bioinformatics · 2026Article
- Analysis of computational tumor-infiltrating lymphocytes in breast cancer from the results of the TIGER challenge.Nature communications · 2026Article
- QuTILs: Open-Source Image-Based Infiltrating Immune Cell Detection for Research Application.Research square · 2026Article
- Focal hotspot and diffuse immune subtypes of tumor-infiltrating lymphocytes: AI-powered spatial clustering classification and its clinical relevance to HER2 expression in triple-negative breast cancer.Journal of translational medicine · 2025Article
- Article
- Deep learning-based fusion of nuclear segmentation features for microsatellite instability and tumor mutational burden prediction in digestive tract cancers: a multicenter validation study.Briefings in bioinformatics · 2025Article
- Development and validation of an artificial intelligence system for triple-negative breast cancer identification and prognosis prediction: a multicentre retrospective study.EClinicalMedicine · 2025Article
- Building digital histology models of transcriptional tumor programs with generative deep learning for pathology-based precision medicine.Genome medicine · 2025Article
- Artificial Intelligence-Based Pathology to Assist Prediction of Neoadjuvant Therapy Responses for Breast Cancer.Cancer medicine · 2025Review
- Decision level scheme for fusing multiomics and histology slide images using deep neural network for tumor prognosis prediction.Scientific reports · 2025Article
- Revolutionizing breast cancer immunotherapy by integrating AI and nanotechnology approaches: review of current applications and future directions.Bioelectronic medicine · 2025Review
- Automated deep learning-based assessment of tumour-infiltrating lymphocyte density determines prognosis in colorectal cancer.Journal of translational medicine · 2025Article
- Deep learning-based spatial analysis on tumor and immune cells of pathology images predicts MIBC prognosis.PloS one · 2025Article
- The role of artificial intelligence in enhancing breast cancer screening and diagnosis: A review of current advances.BioImpacts : BI · 2025Review
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Enhancing AI Research for Breast Cancer: A Comprehensive Review of Tumor-Infiltrating Lymphocyte Datasets.Journal of imaging informatics in medicine · 2024Review
- Deep learning-based approaches for multi-omics data integration and analysis.BioData mining · 2024Review
- Artificial intelligence: illuminating the depths of the tumor microenvironment.Journal of translational medicine · 2024Review
- Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTumor-infiltrating lymphocytes (TILs) and their spatial characterizations on whole-slide images (WSIs) of histopathology sections have become crucial in diagnosis, prognosis, and treatment response prediction for different cancers. However, fully automatic assessment of TILs on WSIs currently remains a great challenge because of the heterogeneity and large size of WSIs. We present an automatic pipeline based on a cascade-training U-net to generate high-resolution TIL maps on WSIs.
methodsWe present global cell-level TIL maps and 43 quantitative TIL spatial image features for 1,000 WSIs of The Cancer Genome Atlas patients with breast cancer. For more specific analysis, all the patients were divided into three subtypes, namely, estrogen receptor (ER)-positive, ER-negative, and triple-negative groups. The associations between TIL scores and gene expression and somatic mutation were examined separately in three breast cancer subtypes. Both univariate and multivariate survival analyses were performed on 43 TIL image features to examine the prognostic value of TIL spatial patterns in different breast cancer subtypes.
resultsThe TIL score was in strong association with immune response pathway and genes (eg, programmed death-1 and
conclusionOur pipeline can facilitate computational pathology-based discovery in cancer immunology and research on immunotherapy. Our analysis results are available for the research community to generate new hypotheses and insights on breast cancer immunology and development.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.