ArticleCancer immunology, immunotherapy : CII2021
Absolute quantification of tumor-infiltrating immune cells in high-grade glioma identifies prognostic and radiomics values.
Article in Cancer immunology, immunotherapy : CII, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 46 citations in OpenAlex.
- T cell immunity in glioma and potential implications for immunotherapy: A systematic review.Neuro-oncology · 2026Pooled it
- Biomarkers for predicting immunotherapy response and resistance in glioblastoma.Frontiers in immunology · 2026Review
- Crosstalk in the brain tumor microenvironment: mechanisms, therapeutic strategies, and clinical advances.Military Medical Research · 2026Review
- The clinical implications and interpretability of computational medical imaging (radiomics) in brain tumors.Insights into imaging · 2025Review
- Predicting postoperative recurrence and survival in glioma patients using enhanced MRI-based delta habitat radiomics: an 8-year retrospective pilot study.World journal of surgical oncology · 2025Article
- MRI radiomics based on machine learning in high-grade gliomas as a promising tool for prediction of CD44 expression and overall survival.Scientific reports · 2025Article
- Radiomics in glioma: emerging trends and challenges.Annals of clinical and translational neurology · 2025Review
- Multiparametric MRI along with machine learning predicts prognosis and treatment response in pediatric low-grade glioma.Nature communications · 2025Article
- From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Cuproptosis-related lncRNAs and genes: Potential markers for glioblastoma prognosis and treatment.PloS one · 2025Article
- MRI-derived radiomics assessing tumor-infiltrating macrophages enable prediction of immune-phenotype, immunotherapy response and survival in glioma.Biomarker research · 2024Article
- Correlation of tumor-associated macrophage infiltration in glioblastoma with magnetic resonance imaging characteristics: a retrospective cross-sectional study.Quantitative imaging in medicine and surgery · 2023Article
- Radiomics for characterization of the glioma immune microenvironment.NPJ precision oncology · 2023Review
- Assessment of brain cancer atlas maps with multimodal imaging features.Journal of translational medicine · 2023Review
- Article
- Investigation of Diagnostic and Prognostic Value of CLEC4M of Non-Small Cell Lung Carcinoma Associated with Immune Microenvironment.International journal of general medicine · 2023Article
- CD93 is Associated with Glioma-related Malignant Processes and Immunosuppressive Cell Infiltration as an Inspiring Biomarker of Survivance.Journal of molecular neuroscience : MN · 2022Article
- Article
- Radiomic Signatures Associated with CD8Cancers · 2022Review
- RecQ mediated genome instability 2 (Aging · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors at 2 institutions in 3 countries.
Funding
Abstract
purposeTo understand the tumor immune microenvironment precisely, it is important to secure the quantified data of tumor-infiltrating immune cells, since the immune cells are true working unit. We analyzed unit immune cell number per unit volume of core tumor tissue of high-grade gliomas (HGG) to correlate their immune microenvironment characteristics with clinical prognosis and radiomic signatures.
methodsThe number of tumor-infiltrating immune cells from 64 HGG core tissue were analyzed using flow cytometry and standardized. After sorting out patient groups according to diverse immune characteristics, the groups were tested if they have any clinical prognostic relevance and specific radiomic signature relationships. Sparse partial least square with discriminant analysis using multimodal magnetic resonance images was employed for all radiomic classifications.
resultsThe median number of CD45 + cells per one gram of HGG core tissue counted 865,770 cells which was equivalent to 8.0% of total cells including tumor cells. There was heterogeneity in the distribution of immune cell subpopulations among patients. Overall survival was significantly better in T cell-deficient group than T cell-enriched group (p = 0.019), and T8 dominant group than T4 dominant group (p = 0.023). The number of tumor-associated macrophages (TAM) and M2-TAM was significantly decreased in isocitrate dehydrogenase mutated HGG. Radiomic signature classification showed good performance in predicting immune phenotypes especially with features extracted from apparent diffusion coefficient maps.
conclusionsAbsolute quantification of tumor-infiltrating immune cells confirmed the heterogeneity of immune microenvironment in HGG which harbors prognostic impact. This immune microenvironment could be predicted by radiomic signatures non-invasively.
Indexed as
Identifiers
What OpenQuestion holds
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.