ArticleNature communications2023
Deep learning of cell spatial organizations identifies clinically relevant insights in tissue images.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Scalable 3D cell-interaction analysis via supercell graphs for prostate cancer risk stratification.bioRxiv : the preprint server for biology · 2026Article
- Dissecting oral premalignant carcinogenesis: spatial omics mechanisms and nanomedicine-driven therapeutic innovation.Molecular cancer · 2026Review
- GNN-EGG: Graph neural network explanations via graph generation.Neurocomputing · 2026Article
- Review
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Leveraging AI for cell biology discovery.Biochemical Society transactions · 2026Review
- Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects.Military Medical Research · 2026Review
- From remote screening to precision prevention: responsible multimodal AI for risk prediction and equitable oral healthcare.Frontiers in oral health · 2026Review
- Breast cancer histopathological image classification based on collaborative multi-domain feature learning.PloS one · 2026Article
- Large language models driven neural architecture search for universal and lightweight disease diagnosis on histopathology slide images.NPJ digital medicine · 2025Article
- A Multi-Layered Framework for Modeling Human Biology: From Basic AI Agents to a Full-Body AI Agent.ArXiv · 2025Article
- Multimodal prediction of tyrosine kinase inhibitors therapy outcomes in advanced EGFR-mutated NSCLC patients.Journal of translational medicine · 2025Article
- Spatial phenotyping of human bronchial airways in obstructive lung disease.Respiratory research · 2025Article
- Systematic scoping review of external validation studies of AI pathology models for lung cancer diagnosis.NPJ precision oncology · 2025Article
- Review
- Systematic inference of super-resolution cell spatial profiles from histology images.Nature communications · 2025Article
- Review
- A large histological images dataset of gastric cancer with tumour microenvironment annotation for AI.Scientific data · 2025Article
- Modulating versatile pathways using a cleavable PEG shell and EGFR-targeted nanoparticles to deliver CRISPR-Cas9 and docetaxel for triple-negative breast cancer inhibition.Archives of pharmacal research · 2024Article
- MVST: Identifying spatial domains of spatial transcriptomes from multiple views using multi-view graph convolutional networks.PLoS computational biology · 2024Article
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15 authors.
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Abstract
Recent advancements in tissue imaging techniques have facilitated the visualization and identification of various cell types within physiological and pathological contexts. Despite the emergence of cell-cell interaction studies, there is a lack of methods for evaluating individual spatial interactions. In this study, we introduce Ceograph, a cell spatial organization-based graph convolutional network designed to analyze cell spatial organization (for example,. the cell spatial distribution, morphology, proximity, and interactions) derived from pathology images. Ceograph identifies key cell spatial organization features by accurately predicting their influence on patient clinical outcomes. In patients with oral potentially malignant disorders, our model highlights reduced structural concordance and increased closeness in epithelial substrata as driving features for an elevated risk of malignant transformation. In lung cancer patients, Ceograph detects elongated tumor nuclei and diminished stroma-stroma closeness as biomarkers for insensitivity to EGFR tyrosine kinase inhibitors. With its potential to predict various clinical outcomes, Ceograph offers a deeper understanding of biological processes and supports the development of personalized therapeutic strategies.
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