ArticleCancer research2020
Computational Staining of Pathology Images to Study the Tumor Microenvironment in Lung Cancer.
Article in Cancer research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 papers.
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Who cites it
84 citing papers in PubMed, 144 citations in OpenAlex.
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- Unraveling the molecular-pathological characteristics and cellular complexity of the tumor immune microenvironment in metastatic non-small cell lung cancer.Cell communication and signaling : CCS · 2025Review
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- Automated cell annotation and classification on histopathology for spatial biomarker discovery.Nature communications · 2025Article
- Combining graph neural network and Mamba to capture local and global tissue spatial relationships in whole slide images.Scientific reports · 2025Article
- Single-cell multimodal analysis reveals tumor microenvironment predictive of treatment response in non-small cell lung cancer.Science advances · 2025Article
- Bringing AI to Clinicians: Simplifying Pleural Effusion Cytology Diagnosis with User-Friendly Models.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions.Biomedicines · 2025Review
- Technical note: Impact of tissue section thickness on accuracy of cell classification with a deep learning network.Journal of pathology informatics · 2025Article
- Stage-dependent spatial distribution and prognostic value of CD8NPJ precision oncology · 2025Article
24 more citing papers are in PubMed but not listed here.
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18 authors at 2 institutions in 1 country.
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Abstract
The spatial organization of different types of cells in tumor tissues reveals important information about the tumor microenvironment (TME). To facilitate the study of cellular spatial organization and interactions, we developed Histology-based Digital-Staining, a deep learning-based computation model, to segment the nuclei of tumor, stroma, lymphocyte, macrophage, karyorrhexis, and red blood cells from standard hematoxylin and eosin-stained pathology images in lung adenocarcinoma. Using this tool, we identified and classified cell nuclei and extracted 48 cell spatial organization-related features that characterize the TME. Using these features, we developed a prognostic model from the National Lung Screening Trial dataset, and independently validated the model in The Cancer Genome Atlas lung adenocarcinoma dataset, in which the predicted high-risk group showed significantly worse survival than the low-risk group (
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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.