ArticleNature communications2025
AI-powered spatial cell phenomics enhances risk stratification in non-small cell lung cancer.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Bridging precision agriculture and human medicine through comparative genetics.Nature reviews. Genetics · 2026Review
- Spatial omics illuminates tumor heterogeneity.Cell reports methods · 2026Review
- Spatial ecotype in tumor immune exclusion: from spatial architecture to therapeutic strategies.Molecular cancer · 2026Review
- ADC Target Profiling in NSCLC: Generalizable AI Separates TROP-2 and cMET Phenotypes.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- Nanomaterials targeting cancer-associated fibroblasts to overcome stromal barriers in cancer immunotherapy.Journal of nanobiotechnology · 2026Review
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- Angiogenic Imbalance Defines Multisystem Phenotypes of Preeclampsia: A Phenotype-Oriented Cohort Study.Clinics and practice · 2026Article
- SpatialFusion: A lightweight multimodal foundation model for pathway-informed spatial niche mapping.bioRxiv : the preprint server for biology · 2026Article
- Estrogen Receptor-Low Positive (ER-Low) Breast Cancer: A Unique Clinical and Pathological Entity.Current oncology (Toronto, Ont.) · 2026Review
- Medea: An omics AI agent for therapeutic discovery.bioRxiv : the preprint server for biology · 2026Article
- Artificial intelligence in non-small cell lung cancer: transforming diagnosis, treatment, and prognostic evaluation.Frontiers in medicine · 2026Review
- From Simple Scores to Intelligent Systems: Encouraging the Development, Validation and Adoption of Robust Prognostic Tools in Small Cell Lung Cancer.Technology in cancer research & treatmentReview
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29 authors.
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Abstract
Risk stratification remains a critical challenge in non-small cell lung cancer patients for optimal therapy selection. In this study, we develop an artificial intelligence-powered spatial cellomics approach that combines histology, multiplex immunofluorescence imaging and multimodal machine learning to characterize the complex cellular relationships of 43 cell phenotypes in the tumor microenvironment in a real-world retrospective cohort of 1168 non-small cell lung cancer patients from two large German cancer centers. The model identifies cell niches associated with survival and achieves a 14% and 47% improvement in risk stratification in the two main non-small cell lung cancer subtypes, lung adenocarcinoma and squamous cell carcinoma, respectively, combining niche patterns with conventional cancer staging. Our results show that complex immune cell niche patterns identify potentially undertreated high-risk patients qualifying for adjuvant therapy. Our approach highlights the potential of artificial intelligence powered multiplex imaging analyses to better understand the contribution of the tumor microenvironment to cancer progression and to improve risk stratification and treatment selection in non-small cell lung cancer.
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