ArticleCommunications medicine2024
Multi-resolution deep learning characterizes tertiary lymphoid structures and their prognostic relevance in solid tumors.
Article in Communications medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
43 citing papers in PubMed, 40 citations in OpenAlex.
- spaGFM is a scalable graph foundation model for spatial transcriptomics analyses.Research square · 2026Article
- Tertiary lymphoid structures in cancer: their development, composition, clinical prognosis, and the drivers of the anti-tumor responses.Molecular biomedicine · 2026Review
- Review
- Review
- Spatial Immune Coding in Tumor-Draining Lymph Nodes: Functional Compartmentalization of Immune Activation and Immunosuppression.International journal of molecular sciences · 2026Review
- TLS as Predictors and Targets in Neoadjuvant Chemoimmunotherapy for NSCLC.Thoracic cancer · 2026Review
- GCUNet: a graph neural network-based contextual learning network for tertiary lymphoid structure semantic segmentation in whole slide image.Visual computing for industry, biomedicine, and art · 2026Article
- Spatial architecture of tertiary lymphoid structures represents an independent prognostic dimension in hepatocellular carcinoma.Journal for immunotherapy of cancer · 2026Article
- Digital Pathology and the AI-Based Quantification of the Tumor Microenvironment in Gastrointestinal Cancer: From Tumor Budding and Tumor-Infiltrating Lymphocytes to Tertiary Lymphoid Structures.International journal of molecular sciences · 2026Review
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Topological Data Analysis for Unsupervised Feature Selection in Large Scale Spatial Omics Data Sets.Bulletin of mathematical biology · 2026Article
- Robust analysis of the tumor spectrum in a preclinical model of breast cancer reveals stable subtypes with distinct growth patterns.Computer methods and programs in biomedicine · 2026Article
- The life cycle of tertiary lymphoid structures in pancreatic cancer-a window of opportunity for immunotherapy.Frontiers in immunology · 2026Review
- Artificial intelligence-enabled multi-omics biomarkers for immune checkpoint blockade: mechanisms, predictive modeling, and clinical translation.Frontiers in immunology · 2026Review
- Clinical impact and therapeutic potential of tertiary lymphoid structures in melanoma.Frontiers in immunology · 2026Review
- Tertiary lymphoid structures in genitourinary cancers: a comprehensive review.Frontiers in oncology · 2026Review
- DenseUNet for Breast Cancer Segmentation in Histopathological Images.Journal of medical signals and sensors · 2026Article
- The biological characteristics of tertiary lymphoid structures in head and neck cancer.Frontiers in immunology · 2026Review
- Tertiary lymphoid structure topotypes in intrahepatic cholangiocarcinoma: spatial immunity, checkpoint-response associations, and therapeutic reprogramming.Frontiers in immunology · 2026Review
- Comparative Analysis of Pathology Foundation Models for Automated Detection of Tertiary Lymphoid Structures in Hematoxylin-and-Eosin-Stained Digital Pathology Images.Computational and structural biotechnology journal · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTertiary lymphoid structures (TLSs) are dense accumulations of lymphocytes in inflamed peripheral tissues, including cancer, and are associated with improved survival and response to immunotherapy in various solid tumors. Histological TLS quantification has been proposed as a novel predictive and prognostic biomarker, but lack of standardized methods of TLS characterization hampers assessment of TLS densities across different patients, diseases, and clinical centers.
methodsWe introduce an approach based on HookNet-TLS, a multi-resolution deep learning model, for automated and unbiased TLS quantification and identification of germinal centers in routine hematoxylin and eosin stained digital pathology slides. We developed HookNet-TLS using n = 1019 manually annotated TCGA slides from clear cell renal cell carcinoma, muscle-invasive bladder cancer, and lung squamous cell carcinoma.
resultsHere we show that HookNet-TLS automates TLS quantification across multiple cancer types achieving human-level performance and demonstrates prognostic associations similar to visual assessment.
conclusionsHookNet-TLS has the potential to be used as a tool for objective quantification of TLS in routine H&E digital pathology slides. We make HookNet-TLS publicly available to promote its use in research.
Identifiers
What OpenQuestion holds
Registered trials
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.