ArticleFrontiers in oncology2018
Quantitative Characterization of CD8+ T Cell Clustering and Spatial Heterogeneity in Solid Tumors.
Article in Frontiers in oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
What it found
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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.
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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
31 citing papers in PubMed, 41 citations in OpenAlex.
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- A hybrid PKPD agent-based model of the tumour immune interaction: effects of anti-cancer combination therapy.Journal of pharmacokinetics and pharmacodynamics · 2026Article
- Survival benefit and dynamics of CD8Frontiers in immunology · 2026Article
- Collagen disorder architecture features are associated with clinical, molecular, genetic factors and survival outcomes in colon cancer.NPJ precision oncology · 2025Article
- Artificial intelligence (AI) for tumor microenvironment (TME) and tumor budding (TB) identification in colorectal cancer (CRC) patients: A systematic review.Journal of pathology informatics · 2024Review
- Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology.Briefings in bioinformatics · 2024Review
- Impact of functional electrical stimulation on nerve-damaged muscles by quantifying fat infiltration using deep learning.Scientific reports · 2024Article
- Article
- Distinct spatial immune microlandscapes are independently associated with outcomes in triple-negative breast cancer.Nature communications · 2023Article
- Biological insights and novel biomarker discovery through deep learning approaches in breast cancer histopathology.NPJ breast cancer · 2023Review
- Quantitative Spatial Profiling of Immune Populations in Pancreatic Ductal Adenocarcinoma Reveals Tumor Microenvironment Heterogeneity and Prognostic Biomarkers.Cancer research · 2022Article
- Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images.IEEE transactions on medical imaging · 2022Article
- Simulations of tumor growth and response to immunotherapy by coupling a spatial agent-based model with a whole-patient quantitative systems pharmacology model.PLoS computational biology · 2022Article
- Tumor immune microenvironment of primary prostate cancer with and without germline mutations in homologous recombination repair genes.Journal for immunotherapy of cancer · 2022Article
- Tumor-immune landscape patterns before and after chemoradiation in resectable esophageal adenocarcinomas.The Journal of pathology · 2022Article
- Article
- Artificial intelligence for precision medicine in autoimmune liver disease.Frontiers in immunology · 2022Review
- Digital pathology and artificial intelligence in translational medicine and clinical practice.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2022Review
- Machine-Learning-Based Evaluation of Intratumoral Heterogeneity and Tumor-Stroma Interface for Clinical Guidance.The American journal of pathology · 2021Review
- Predictive models of response to neoadjuvant chemotherapy in muscle-invasive bladder cancer using nuclear morphology and tissue architecture.Cell reports. Medicine · 2021Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
Abstract
Quantitative characterization of the tumor microenvironment, including its immuno-architecture, is important for developing quantitative diagnostic and predictive biomarkers, matching patients to the most appropriate treatments for precision medicine, and for providing quantitative data for building systems biology computational models able to predict tumor dynamics in the context of immune checkpoint blockade therapies. The intra- and inter-tumoral spatial heterogeneities are potentially key to the understanding of the dose-response relationships, but they also bring challenges to properly parameterizing and validating such models. In this study, we developed a workflow to detect CD8+ T cells from whole slide imaging data, and quantify the spatial heterogeneity using multiple metrics by applying spatial point pattern analysis and morphometric analysis. The results indicate a higher intra-tumoral heterogeneity compared with the heterogeneity across patients. By comparing the baseline metrics with PD-1 blockade treatment outcome, our results indicate that the number of high-density T cell clusters of both circular and elongated shapes are higher in patients who responded to the treatment. This methodology can be applied to quantitatively characterize the tumor microenvironment, including immuno-architecture, and its heterogeneity for different cancer types.
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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.