ArticleNPJ precision oncology2024
Deep learning on tertiary lymphoid structures in hematoxylin-eosin predicts cancer prognosis and immunotherapy response.
Article in NPJ precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.
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
47 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Artificial intelligence-powered spatial analysis of tumor microenvironment in patients with non-small cell lung cancer with acquired resistance to EGFR tyrosine kinase inhibitor.Journal for immunotherapy of cancer · 2025Trial
- 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
- Integrative machine learning reveals a TLS signature and CCL5-CCR1 axis-associated immune remodeling in breast cancer.Cellular oncology (Dordrecht, Netherlands) · 2026Article
- Prognostic value of tertiary lymphoid structures in high-risk esophageal squamous cell carcinoma following neoadjuvant chemoimmunotherapy.BMC cancer · 2026Article
- Regulatory B-cell states in NSCLC immunotherapy resistance: mechanisms, spatial context and translational implications.Cancer immunology, immunotherapy : CII · 2026Review
- Tertiary lymphoid structures in breast cancer: Mechanistic insights, subtype-specific patterns, and therapeutic implications.World journal of surgical oncology · 2026Review
- Tumor infiltrating B cells and tertiary lymphoid structures in pancreatic cancer prognosis and therapy.iScience · 2026Review
- AI in esophageal cancer: advances, barriers to clinical translation, and perspectives for digital health.Journal of translational medicine · 2026Review
- Review
- T cell fate regulation in EBV‑associated nasopharyngeal carcinoma (Review).Oncology reports · 2026Review
- A deep learning-based digital biopsy for predicting early recurrence in gastric cancer.Nature communications · 2026Article
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- TLScope: a deep learning framework for quantifying tertiary lymphoid structures from H&E images reveals prognostic heterogeneity across breast cancer subtypes.Breast cancer research : BCR · 2026Article
- Cuproptosis-associated PDHA1 promotes sarcoma progression and immunotherapy responsiveness via the E2F1-PD-L1 axis: a multi-omics and clinical validation study.NPJ precision oncology · 2026Article
- Self-supervised pretraining with NuSPIRe unlocks nuclear morphology-driven insights in spatial omics.Genome biology · 2026Article
- 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
- Crosstalk in the brain tumor microenvironment: mechanisms, therapeutic strategies, and clinical advances.Military Medical Research · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
26 authors.
Funding
Abstract
Tertiary lymphoid structures (TLSs) have been associated with favorable immunotherapy responses and prognosis in various cancers. Despite their significance, their quantification using multiplex immunohistochemistry (mIHC) staining of T and B lymphocytes remains labor-intensive, limiting its clinical utility. To address this challenge, we curated a dataset from matched mIHC and H&E whole-slide images (WSIs) and developed a deep learning model for automated segmentation of TLSs. The model achieved Dice coefficients of 0.91 on the internal test set and 0.866 on the external validation set, along with intersection over union (IoU) scores of 0.819 and 0.787, respectively. The TLS ratio, defined as the segmented TLS area over the total tissue area, correlated with B lymphocyte levels and the expression of CXCL13, a chemokine associated with TLS formation, in 6140 patients spanning 16 tumor types from The Cancer Genome Atlas (TCGA). The prognostic models for overall survival indicated that the inclusion of the TLS ratio with TNM staging significantly enhanced the models' discriminative ability, outperforming the traditional models that solely incorporated TNM staging, in 10 out of 15 TCGA tumor types. Furthermore, when applied to biopsied treatment-naïve tumor samples, higher TLS ratios predicted a positive immunotherapy response across multiple cohorts, including specific therapies for esophageal squamous cell carcinoma, non-small cell lung cancer, and stomach adenocarcinoma. In conclusion, our deep learning-based approach offers an automated and reproducible method for TLS segmentation and quantification, highlighting its potential in predicting immunotherapy response and informing cancer prognosis.
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.