ArticleNPJ precision oncology2022
Cell graph neural networks enable the precise prediction of patient survival in gastric cancer.
Article in NPJ precision oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Scalable 3D cell-interaction analysis via supercell graphs for prostate cancer risk stratification.bioRxiv : the preprint server for biology · 2026Article
- Hierarchical Tissue-Specific Modeling of Pathology Images Predicts Response in HER2+ Breast Cancer.Cyborg and bionic systems (Washington, D.C.) · 2026Article
- Encoding functional edges in graphs to model spatially varying relationships in the tumor microenvironment.NPJ artificial intelligence · 2026Article
- Deep learning-based fusion of nuclear segmentation features for microsatellite instability and tumor mutational burden prediction in digestive tract cancers: a multicenter validation study.Briefings in bioinformatics · 2025Article
- Multidimensional decoding of colorectal cancer heterogeneity: Artificial intelligence-enabled precision exploration of single-cell and spatial transcriptomics.World journal of gastrointestinal oncology · 2025Review
- Graph neural networks learn emergent tissue properties from spatial molecular profiles.Nature communications · 2025Article
- Article
- Bi-level graph learning unveils prognosis-relevant tumor microenvironment patterns in breast multiplexed digital pathology.Patterns (New York, N.Y.) · 2025Article
- A large histological images dataset of gastric cancer with tumour microenvironment annotation for AI.Scientific data · 2025Article
- Edges are all you need: Potential of medical time series analysis on complete blood count data with graph neural networks.PloS one · 2025Article
- Unravelling key genes and molecular pathways in gastric inflammation-to-cancer transition through causal discovery: implications for early diagnosis and therapy.SAGE open medicine · 2025Article
- Cytokine profiles as predictors of HIV incidence using machine learning survival models and statistical interpretable techniques.Scientific reports · 2024Article
- Advancing prognostic precision in gastric cancer with an immunoinflammatory index.World journal of gastroenterology · 2024Article
- Self-guided Knowledge-Injected Graph Neural Network for Alzheimer's Diseases.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology.Briefings in bioinformatics · 2024Review
- Using random forests to uncover the predictive power of distance-varying cell interactions in tumor microenvironments.PLoS computational biology · 2024Article
- Cancer drug sensitivity estimation using modular deep Graph Neural Networks.NAR genomics and bioinformatics · 2024Article
- Spatial Morphoproteomic Features Predict Uniqueness of Immune Microarchitectures and Responses in Lymphoid Follicles.bioRxiv : the preprint server for biology · 2024Article
- Development and validation of a survival prediction model for patients with advanced non-small cell lung cancer based on LASSO regression.Frontiers in immunology · 2024Article
- Prediction of Tuberculosis From Lung Tissue Images of Diversity Outbred Mice Using Jump Knowledge Based Cell Graph Neural Network.IEEE access : practical innovations, open solutions · 2024Article
Corrections and comments
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gastric cancer is one of the deadliest cancers worldwide. An accurate prognosis is essential for effective clinical assessment and treatment. Spatial patterns in the tumor microenvironment (TME) are conceptually indicative of the staging and progression of gastric cancer patients. Using spatial patterns of the TME by integrating and transforming the multiplexed immunohistochemistry (mIHC) images as Cell-Graphs, we propose a graph neural network-based approach, termed Cell-Graph Signature or CG
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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.