ArticleCell reports. Medicine2024
Harnessing TME depicted by histological images to improve cancer prognosis through a deep learning system.
Article in Cell reports. Medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transforming histologic assessment: artificial intelligence in cancer diagnosis and personalized treatment.British journal of cancer · 2025Pooled it
- Angiopoietin-like protein 4 deficiency promotes tumor immune infiltration and CD8Molecular therapy. Oncology · 2026Article
- ALPaCA: Adapting Llama for Pathology Context Analysis to enable slide-level question answering.Nature communications · 2026Article
- Development and validation of artificial intelligence-based model for bladder cancer immunophenotyping using whole slide images.NPJ precision oncology · 2026Article
- Meta-encoder: a unified integration framework for multiple pathological foundation models in cancer detection.Nature communications · 2026Article
- Artificial intelligence models: transforming early diagnosis and precise treatment of gastrointestinal cancers.Molecular cancer · 2026Review
- Prognostic model for pancreatic cancer based on machine learning of routine slides and transcriptomic tumor analysis.British journal of cancer · 2026Article
- Foundation Model-Enabled Multimodal Deep Learning for Prognostic Prediction in Colorectal Cancer with Incomplete Modalities: A Multi-Institutional Retrospective Study.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- HiST: Histological Images Reconstruct Tumor Spatial Transcriptomics via MultiScale Fusion Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- sCellST predicts single-cell gene expression from H& E images.Nature communications · 2026Article
- AI in Cancer Prognosis: A Systematic Review of Multimodal Models Combining Pathology Images and High-Throughput Omics.Cancer informatics · 2026Article
- Quantification-based explainable artificial intelligence for deep learning decisions: clustering and visualization of quantitative morphometric features in hepatocellular carcinoma discrimination.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- An Explainable Multimodal Artificial Intelligence Model Integrating Histopathological Microenvironment and EHR Phenotypes for Germline Genetic Testing in Breast Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Spatial histology and gene-expression representation and generative learning via online self-distillation contrastive learning.Briefings in bioinformatics · 2025Article
- Combining spatial transcriptomics with tissue morphology.Nature communications · 2025Review
- Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis.Genomics, proteomics & bioinformatics · 2025Review
- Unravelling tumour spatiotemporal heterogeneity using spatial multimodal data.Clinical and translational medicine · 2025Review
- Artificial intelligence in gastrointestinal cancers: Diagnostic, prognostic, and surgical strategies.Cancer letters · 2025Review
- A large histological images dataset of gastric cancer with tumour microenvironment annotation for AI.Scientific data · 2025Article
- Intratumoral microbiota, fatty acid metabolism, and tumor microenvironment constitute an unresolved trinity in colon adenocarcinoma.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spatial transcriptomics (ST) provides insights into the tumor microenvironment (TME), which is closely associated with cancer prognosis, but ST has limited clinical availability. In this study, we provide a powerful deep learning system to augment TME information based on histological images for patients without ST data, thereby empowering precise cancer prognosis. The system provides two connections to bridge existing gaps. The first is the integrated graph and image deep learning (IGI-DL) model, which predicts ST expression based on histological images with a 0.171 increase in mean correlation across three cancer types compared with five existing methods. The second connection is the cancer prognosis prediction model, based on TME depicted by spatial gene expression. Our survival model, using graphs with predicted ST features, achieves superior accuracy with a concordance index of 0.747 and 0.725 for The Cancer Genome Atlas breast cancer and colorectal cancer cohorts, outperforming other survival models. For the external Molecular and Cellular Oncology colorectal cancer cohort, our survival model maintains a stable advantage.
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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.