ArticleNature cancer2025
Spatially resolved transcriptomics and graph-based deep learning improve accuracy of routine CNS tumor diagnostics.
Article in Nature cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Spatial ecotype in tumor immune exclusion: from spatial architecture to therapeutic strategies.Molecular cancer · 2026Review
- SPP1-driven spatial niche remodelling defines aggressive progression in the basal subtype of upper tract urothelial carcinoma.Clinical and translational medicine · 2026Article
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Glioma-intrinsic MAPK/ERK signaling promotes immunotherapy efficacy through T cell infiltration and interferon responses.Nature communications · 2026Article
- From Spatial Epigenomes to Clinical Diagnostics: Integrative Methylomics Across Scales and Modalities.International journal of molecular sciences · 2026Review
- Inflammation-Driven Lymphoid Structures: Organization, Function, and Clinical Impact Across Autoimmunity, Cancer, and Checkpoint Toxicity.Immunological reviews · 2026Review
- SR2P: an efficient stacking method to predict protein abundance from gene expression in spatial transcriptomics data.bioRxiv : the preprint server for biology · 2026Article
- Spatial transcriptomics and artificial intelligence: a scoping review of emerging applications in head and neck pathology.Head and neck pathology · 2026Article
- AI-Based Methods in Neuropathology for Diagnosis and Treatment of Brain Tumors.European journal of neurology · 2026Review
- A microenvironment-determined risk continuum refines subtyping in meningioma and reveals determinants of machine learning-based tumor classification.Nature genetics · 2026Article
- Areas of research priorities in epilepsy: A position paper of the European Reference Network for Rare and Complex Epilepsies, EpiCARE.Epilepsia open · 2026Article
- Co-mapping clonal and transcriptional heterogeneity in somatic evolution via GoT-Multi.Cell genomics · 2026Article
- Spatial AI in cancer: mapping immune evasion topology through multi-modal omics and deep learning.Frontiers in oncology · 2026Review
- Combining spatial transcriptomics and AI to enhance brain tumour diagnosis.Nature reviews. Cancer · 2025Article
- Current Role and Future Frontiers of Spatial Transcriptomics in Genitourinary Cancers.Cancers · 2025Review
- Precision Neuro-Oncology in Glioblastoma: AI-Guided CRISPR Editing and Real-Time Multi-Omics for Genomic Brain Surgery.International journal of molecular sciences · 2025Review
- Spatially resolved transcriptomics and graph-based deep learning improve accuracy of routine CNS tumor diagnostics.Nature cancer · 2025Article
- Clinicopathologic and Molecular Characterization of Colorectal Cancer in Patients Aged ≥80 years.Clinical interventions in aging · 2025Article
- PRTS: Predicting Single-Cell Spatial Transcriptomic Maps from Histological Images.Research (Washington, D.C.) · 2025Article
- Neuronal precursor cell persistence in ganglioglioma is associated with extracellular matrix remodeling and immune cell infiltration.Neuro-oncology advancesArticle
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Authors and funding
28 authors.
Funding
Abstract
The diagnostic landscape of brain tumors integrates comprehensive molecular markers alongside traditional histopathological evaluation. DNA methylation and next-generation sequencing (NGS) have become a cornerstone in central nervous system (CNS) tumor classification. A limiting requirement for NGS and methylation profiling is sufficient DNA quality and quantity, which restrict its feasibility. Here we demonstrate NePSTA (neuropathology spatial transcriptomic analysis) for comprehensive morphological and molecular neuropathological diagnostics from single 5-µm tissue sections. NePSTA uses spatial transcriptomics with graph neural networks for automated histological and molecular evaluations. Trained and evaluated across 130 participants with CNS malignancies and healthy donors across four medical centers, NePSTA predicts tissue histology and methylation-based subclasses with high accuracy. We demonstrate the ability to reconstruct immunohistochemistry and genotype profiling on tissue with minimal requirements, inadequate for conventional molecular diagnostics, demonstrating the potential to enhance tumor subtype identification with implications for fast and precise diagnostic workup.
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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.