ArticleNature communications2025
Systematic inference of super-resolution cell spatial profiles from histology images.
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 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparative diagnostic accuracy of different artificial intelligence models for early gastric cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Improving cellular nuclei detection in histological images by combining super-resolution techniques and deep learning-based object detectors.Medical & biological engineering & computing · 2026Article
- Integrating pathological morphologies and molecular profiles for clinical biomarker discovery with unified multimodal embedding.Clinical and translational medicine · 2026Article
- TIMEL: Deep learning-statistical integration reveals spatial stromal and immune signatures of aggressive colon cancer.Journal of pathology informatics · 2026Article
- STAID: A Self-Refining Deep Learning Framework for Spatial Cell-Type Deconvolution with Biologically Informed Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Human genetics across levels of biological organization.Nature reviews. Genetics · 2026Review
- Systematically decoding pathological morphologies and molecular profiles with unified multimodal embedding.Nature methods · 2026Article
- A conceptual blueprint for "turning cold to hot" in Osteosarcoma: from TME stratification hypotheses to adaptive therapeutic prospects.Cell communication and signaling : CCS · 2026Review
- Artificial intelligence models: transforming early diagnosis and precise treatment of gastrointestinal cancers.Molecular cancer · 2026Review
- Large-Scale Histological Image Dataset with Metadata for Colorectal Cancer Microenvironment.Scientific data · 2026Article
- GastroTCM: a large language model assistant for gastroenterology in traditional Chinese medicine.Chinese medicine · 2026Article
- Artificial Intelligence in Traditional Chinese Medicine: Unraveling Herbal Medicine's Mechanisms.Research (Washington, D.C.) · 2026Review
- Multi-regional feature integration on enhanced CT for lymph node metastasis prediction in gastric cancer: a novel radiomics approach.BMC medical imaging · 2025Article
- Integrating AI, Machine Learning, and Animal Models for Precision Oncology: Bridging Preclinical and Clinical Gaps.ACS pharmacology & translational science · 2025Article
- Multi-Omics Meets Premalignancy: Paving the Way for Early Prevention of Cancer.Research (Washington, D.C.) · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Inferring cell spatial profiles from histology images is critical for cancer diagnosis and treatment in clinical settings. In this study, we report a weakly-supervised deep-learning method, HistoCell, to directly infer super-resolution cell spatial profiles consisting of cell types, cell states and their spatial network from histology images at the single-nucleus-level. Benchmark analysis demonstrates that HistoCell robustly achieves state-of-the-art performance in terms of cell type/states prediction solely from histology images across multiple cancer tissues. HistoCell can significantly enhance the deconvolution accuracy for the spatial transcriptomics data and enable accurate annotation of subtle cancer tissue architectures. Moreover, HistoCell is applied to de novo discovery of clinically relevant spatial organization indicators, including prognosis and drug response biomarkers, across diverse cancer types. HistoCell also enable image-based screening of cell populations that drives phenotype of interest, and is applied to discover the cell population and corresponding spatial organization indicators associated with gastric malignant transformation risk. Overall, HistoCell emerges as a powerful and versatile tool for cancer studies in histology image-only cohorts.
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