ArticleNature communications2022
Intratumor graph neural network recovers hidden prognostic value of multi-biomarker spatial heterogeneity.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The association between tumor-stromal collagen features and the clinical outcomes of patients with breast cancer: a systematic review.Breast cancer research : BCR · 2025Pooled it
- From Label-Free Multiphoton Imaging to Pathological Reports: A Vision-Language Breast Cancer Margin Pathological Diagnosis System.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Spatial anchoring of lipid metabolism shapes immune fate in the tumor microenvironment.Lipids in health and disease · 2026Review
- Development and validation of artificial intelligence-based model for bladder cancer immunophenotyping using whole slide images.NPJ precision oncology · 2026Article
- Discovering proteo-transcriptomic networks via biologically informed heterogeneous graph learning.Nucleic acids research · 2026Article
- Artificial intelligence for triple-negative breast cancer from imaging to multi-omics.Frontiers in oncology · 2026Review
- Spatial AI in cancer: mapping immune evasion topology through multi-modal omics and deep learning.Frontiers in oncology · 2026Review
- On the importance of simultaneous label-free multimodal nonlinear optical imaging for biomedical applications.APL photonics · 2025Article
- Glucocorticoid-Mediated Extracellular Matrix Regulation: Implications for Precision Therapy.Biomedicines · 2025Review
- Review
- Article
- Imaging-based assessment of response to olaparib in platinum-sensitive relapsed ovarian cancer patients.Frontiers in oncology · 2025Article
- Construction of a prognostic model for disulfidptosis related ferroptosis genes lung adenocarcinoma and the role of DECR1 in lung adenocarcinoma.Frontiers in immunology · 2025Article
- Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy.Light, science & applications · 2024Review
- "Patchiness" in mechanical stiffness across a tumor as an early-stage marker for malignancy.BMC ecology and evolution · 2024Article
- Review
- GNN-surv: Discrete-Time Survival Prediction Using Graph Neural Networks.Bioengineering (Basel, Switzerland) · 2023Article
- Intratumor graph neural network recovers hidden prognostic value of multi-biomarker spatial heterogeneity.Nature communications · 2022Article
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14 authors.
Funding
Abstract
Biomarkers are indispensable for precision medicine. However, focused single-biomarker development using human tissue has been complicated by sample spatial heterogeneity. To address this challenge, we tested a representation of primary tumor that synergistically integrated multiple in situ biomarkers of extracellular matrix from multiple sampling regions into an intratumor graph neural network. Surprisingly, the differential prognostic value of this computational model over its conventional non-graph counterpart approximated that of combined routine prognostic biomarkers (tumor size, nodal status, histologic grade, molecular subtype, etc.) for 995 breast cancer patients under a retrospective study. This large prognostic value, originated from implicit but interpretable regional interactions among the graphically integrated in situ biomarkers, would otherwise be lost if they were separately developed into single conventional (spatially homogenized) biomarkers. Our study demonstrates an alternative route to cancer prognosis by taping the regional interactions among existing biomarkers rather than developing novel biomarkers.
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