ArticleNature communications2024
A multicenter proof-of-concept study on deep learning-based intraoperative discrimination of primary central nervous system lymphoma.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Lymphoma Histopathology: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Artificial intelligence reshaping the paradigm of hematologic malignancy diagnosis and treatment: From static assessment to dynamic precision management.Annals of hematology · 2026Review
- A deep learning model for the diagnosis of gastric neuroendocrine carcinoma.Communications medicine · 2026Article
- AI-Powered Histology for Molecular Profiling in Brain Tumors: Toward Smart Diagnostics from Tissue.Cancers · 2025Review
- Quantification-based explainable artificial intelligence for deep learning decisions: clustering and visualization of quantitative morphometric features in hepatocellular carcinoma discrimination.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Review
- The Role of Artificial Intelligence and Radiomics in the Management of Lymphomas by PET/CT: The Clairvoyance in Clinic.Cancer management and research · 2025Review
- Weakly supervised pathological differentiation of primary central nervous system lymphoma and glioblastoma on multi-site whole slide images.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Machine learning methods for histopathological image analysis: Updates in 2024.Computational and structural biotechnology journal · 2025Review
Corrections and comments
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate intraoperative differentiation of primary central nervous system lymphoma (PCNSL) remains pivotal in guiding neurosurgical decisions. However, distinguishing PCNSL from other lesions, notably glioma, through frozen sections challenges pathologists. Here we sought to develop and validate a deep learning model capable of precisely distinguishing PCNSL from non-PCNSL lesions, especially glioma, using hematoxylin and eosin (H&E)-stained frozen whole-slide images. Also, we compared its performance against pathologists of varying expertise. Additionally, a human-machine fusion approach integrated both model and pathologic diagnostics. In external cohorts, LGNet achieved AUROCs of 0.965 and 0.972 in distinguishing PCNSL from glioma and AUROCs of 0.981 and 0.993 in differentiating PCNSL from non-PCNSL lesions. Outperforming several pathologists, LGNet significantly improved diagnostic performance, further augmented to some extent by fusion approach. LGNet's proficiency in frozen section analysis and its synergy with pathologists indicate its valuable role in intraoperative diagnosis, particularly in discriminating PCNSL from glioma, alongside other lesions.
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