ArticleCell2026
Multimodal AI generates virtual population for tumor microenvironment modeling.
Article in Cell, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
38 citing papers in PubMed.
- Trial
- Article
- A global digital navigator of human health for precision medicine.Nature medicine · 2026Article
- Heterogeneity in cancer: molecular mechanisms and therapeutic strategies.Signal transduction and targeted therapy · 2026Review
- Review
- Artificial intelligence-assisted histopathological diagnosis of endocervical gastric-type adenocarcinoma: a multicenter model development and validation study.The journal of pathology. Clinical research · 2026Article
- Longitudinal alignments and syntheses of multimodal clinical data for personalized medicine with the PULSE framework.Nature computational science · 2026Article
- Virtual cells: progress and challenges from mechanistic modeling to data-driven integration.Briefings in bioinformatics · 2026Review
- Artificial intelligence virtual bone organoids (AIVBOs).Journal of orthopaedic translation · 2026Review
- Artificial Intelligence in Ocular Oncology and Pathology: A Call for Contributions.Ocular oncology and pathology · 2026Article
- Multi-Omics Integration Identifies a CDH3-Associated Malignant Epithelial State and Immunosuppressive Niche to Predict Prognosis in Thymic Epithelial Tumors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Integrating pathological morphologies and molecular profiles for clinical biomarker discovery with unified multimodal embedding.Clinical and translational medicine · 2026Article
- Review
- Multimodal spatial omics: From data acquisition to computational integration.Patterns (New York, N.Y.) · 2026Review
- Cellular architecture and neighborhood-informed virtual spatial tumor profiling from histopathology.Cell · 2026Article
- Histopathology-centered computational evolution of spatial omics: integration, mapping, and foundation models.Briefings in bioinformatics · 2026Review
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Multi-omics profiling of pancreatic neuroendocrine tumors: Interplay of genomic instability and the tumor microenvironment (Review).Oncology letters · 2026Review
- Dissecting and directing pathology foundation models.bioRxiv : the preprint server for biology · 2026Article
- Review
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Authors and funding
21 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The tumor immune microenvironment (TIME) critically impacts cancer progression and immunotherapy response. Multiplex immunofluorescence (mIF) is a powerful imaging modality for deciphering TIME, but its applicability is limited by high cost and low throughput. We propose GigaTIME, a multimodal AI framework for population-scale TIME modeling by bridging cell morphology and states. GigaTIME learns a cross-modal translator to generate virtual mIF images from hematoxylin and eosin (H&E) slides by training on 40 million cells with paired H&E and mIF data across 21 proteins. We applied GigaTIME to 14,256 patients from 51 hospitals and over 1,000 clinics across seven US states in Providence Health, generating 299,376 virtual mIF slides spanning 24 cancer types and 306 subtypes. This virtual population uncovered 1,234 statistically significant associations linking proteins, biomarkers, staging, and survival. Such analyses were previously infeasible due to the scarcity of mIF data. Independent validation on 10,200 TCGA patients further corroborated our findings.
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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.