ArticleScientific reports2025
Automated classification of tertiary lymphoid structures in colorectal cancer using TLS-PAT artificial intelligence tool.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Beyond the barrier: Engineering the tumor-immune-soil nexus-a mechanistic blueprint for integrating Traditional Chinese Medicine with immunotherapy in metastatic colorectal cancer.Medical oncology (Northwood, London, England) · 2026Review
- Review
- Tumor infiltrating B cells and tertiary lymphoid structures in pancreatic cancer prognosis and therapy.iScience · 2026Review
- 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
- Tertiary lymphoid structure-related genes drive tumor microenvironment heterogeneity and prognostic disparities in left-Translational cancer research · 2026Article
- 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
- Data-driven precision: artificial intelligence redefining immunoradiotherapy in advanced pancreatic cancer.Frontiers in pharmacology · 2026Review
- High-Performance Silicon Nanowire Array Biosensor for Combined Detection of Colorectal Cancer Biomarkers.Micromachines · 2025Article
- Tertiary lymphoid structures in renal cell carcinoma: from heterogeneity dissection to translational precision immunotherapy.Frontiers in immunology · 2025Review
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colorectal cancer (CRC) ranks as the third most common and second deadliest cancer worldwide. The immune system, particularly tertiary lymphoid structures (TLS), significantly influences CRC progression and prognosis. TLS maturation, especially in the presence of germinal centers, correlates with improved patient outcomes; however, consistent and objective TLS assessment is hindered by varying histological definitions and limitations of traditional staining methods. This study involved 656 patients with colorectal adenocarcinoma from CHU Brest, France. We employed dual immunohistochemistry staining for CD21 and CD23 to classify TLS maturation stages in whole-slide images and implemented a fivefold cross-validation. Using ResNet50 and Vision Transformer models, we compared various aggregation methods, architectures, and pretraining techniques. Our automated system, TLS-PAT, achieved high accuracy (0.845) and robustness (kappa = 0.761) in classifying TLS maturation, particularly with the Vision Transformer pretrained on ImageNet using Max Confidence aggregation. This AI-driven approach offers a standardized method for automated TLS classification, complementing existing detection techniques. Our open-source tools are designed for easy integration with current methods, paving the way for further research in external datasets and other cancer types.
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Registered trials
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