ArticleCancers2020
Radiomic Immunophenotyping of GSEA-Assessed Immunophenotypes of Glioblastoma and Its Implications for Prognosis: A Feasibility Study.
Article in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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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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Who cites it
27 citing papers in PubMed, 42 citations in OpenAlex.
- Glioblastoma radiomics can delineate systemic immune activity states like blood abundance of T cell populations or transcription factors.Journal of neuroinflammation · 2026Article
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- Article
- Opportunities and challenges in application of VASARI features in neuro-oncology.Neuro-oncology practice · 2025Review
- Targeting cancer stem cells with CAR-based immunotherapy: biology, evidence, and future directions.Cancer cell international · 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
- Towards integrating imaging and immunology in glioblastoma: mapping blood immune system metrics to tumor magnetic resonance image data.Acta neuropathologica communications · 2024Article
- Radiogenomics as an Integrated Approach to Glioblastoma Precision Medicine.Current oncology reports · 2024Review
- Review
- Understanding the immunosuppressive microenvironment of glioma: mechanistic insights and clinical perspectives.Journal of hematology & oncology · 2024Review
- MRI-derived radiomics assessing tumor-infiltrating macrophages enable prediction of immune-phenotype, immunotherapy response and survival in glioma.Biomarker research · 2024Article
- Quantitative radiomics analysis of imaging features in adults and children Mycoplasma pneumonia.Frontiers in medicine · 2024Article
- Radiomics for characterization of the glioma immune microenvironment.NPJ precision oncology · 2023Review
- A review of radiomics and genomics applications in cancers: the way towards precision medicine.Radiation oncology (London, England) · 2022Review
- Radiated glioblastoma cell-derived exosomal circ_0012381 induce M2 polarization of microglia to promote the growth of glioblastoma by CCL2/CCR2 axis.Journal of translational medicine · 2022Article
- Radiomic Signatures Associated with CD8Cancers · 2022Review
- Radiogenomic Predictors of Recurrence in Glioblastoma-A Systematic Review.Journal of personalized medicine · 2022Review
- The value of CT radiomic in differentiating mycoplasma pneumoniae pneumonia from streptococcus pneumoniae pneumonia with similar consolidation in children under 5 years.Frontiers in pediatrics · 2022Article
- Radiomics for precision medicine in glioblastoma.Journal of neuro-oncology · 2022Review
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Characterization of immunophenotypes in glioblastoma (GBM) is important for therapeutic stratification and helps predict treatment response and prognosis. Radiomics can be used to predict molecular subtypes and gene expression levels. However, whether radiomics aids immunophenotyping prediction is still unknown. In this study, to classify immunophenotypes in patients with GBM, we developed machine learning-based magnetic resonance (MR) radiomic models to evaluate the enrichment levels of four immune subsets: Cytotoxic T lymphocytes (CTLs), activated dendritic cells, regulatory T cells (Tregs), and myeloid-derived suppressor cells (MDSCs). Independent testing data and the leave-one-out cross-validation method were used to evaluate model effectiveness and model performance, respectively. We identified five immunophenotypes (G1 to G5) based on the enrichment level for the four immune subsets. G2 had the worst prognosis and comprised highly enriched MDSCs and lowly enriched CTLs. G3 had the best prognosis and comprised lowly enriched MDSCs and Tregs and highly enriched CTLs. The average accuracy of T1-weighted contrasted MR radiomics models of the enrichment level for the four immune subsets reached 79% and predicted G2, G3, and the "immune-cold" phenotype (G1) according to our radiomics models. Our radiomic immunophenotyping models feasibly characterize the immunophenotypes of GBM and can predict patient prognosis.
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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.