ReviewNPJ precision oncology2023
Radiomics for characterization of the glioma immune microenvironment.
Review in NPJ precision oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses 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
33 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Imaging of cancer of unknown primary: a systematic literature review of the past, present, and future.The British journal of radiology · 2025Pooled it
- Predicting IDH and ATRX mutations in gliomas from radiomic features with machine learning: a systematic review and meta-analysis.Frontiers in radiology · 2024Pooled it
- Review
- Prediction of motor developmental outcomes based on MRI radiomics in premature infants.Pediatric research · 2026Article
- Advancing the understanding of tumor microenvironment through medical imaging based on bibliometric analysis.Translational cancer research · 2026Article
- Segmentation-Free Multi-Fractal and Radiomics Response Profiling of Live Unstained Confluent Microglia In Vitro Using Phase-Contrast Microscopy Images.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Beyond morphology: imaging the glioblastoma microenvironment in the era of quantitative neuro-oncology.Frontiers in medicine · 2026Review
- AI-driven radiomics and radiogenomics: supporting the assessment and differentiation of pseudoprogression in cellular immunotherapy for glioblastoma.Frontiers in immunology · 2026Review
- Integration of MRI radiomics with immune profiling to predict response to CDK6 inhibitors in high-grade glioma.Frontiers in immunology · 2026Observational
- FKBP10 promotes M2 polarization of macrophage via MEK/ERK/CXCL8 axis and facilitates tumor progression in clear cell renal cell carcinoma.International journal of biological sciences · 2026Article
- Navigating the Tumor Microenvironment in Colorectal Liver Metastasis: Barriers to Therapy and Emerging Opportunities.Oncology research · 2026Review
- Decoding the spatiotemporal dynamics of tumor immune niche remodeling in cancer immunotherapy.Frontiers in immunology · 2026Review
- Opportunities and challenges in application of VASARI features in neuro-oncology.Neuro-oncology practice · 2025Review
- Predictive power of combined inflammatory markers and magnetic resonance imaging features for glioma grading using machine learning: a retrospective study.BMC medical imaging · 2025Article
- Review
- Artificial Intelligence in the Diagnosis and Treatment of Brain Gliomas.Biomedicines · 2025Review
- Radiomics in pediatric brain tumors: from images to insights.Discover oncology · 2025Review
- Review
- Gene regulatory networks analysis for the discovery of prognostic genes in gliomas.Scientific reports · 2025Article
- Imaging genomics of cancer: a bibliometric analysis and review.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Increasing evidence suggests that besides mutational and molecular alterations, the immune component of the tumor microenvironment also substantially impacts tumor behavior and complicates treatment response, particularly to immunotherapies. Although the standard method for characterizing tumor immune profile is through performing integrated genomic analysis on tissue biopsies, the dynamic change in the immune composition of the tumor microenvironment makes this approach not feasible, especially for brain tumors. Radiomics is a rapidly growing field that uses advanced imaging techniques and computational algorithms to extract numerous quantitative features from medical images. Recent advances in machine learning methods are facilitating biological validation of radiomic signatures and allowing them to "mine" for a variety of significant correlates, including genetic, immunologic, and histologic data. Radiomics has the potential to be used as a non-invasive approach to predict the presence and density of immune cells within the microenvironment, as well as to assess the expression of immune-related genes and pathways. This information can be essential for patient stratification, informing treatment decisions and predicting patients' response to immunotherapies. This is particularly important for tumors with difficult surgical access such as gliomas. In this review, we provide an overview of the glioma microenvironment, describe novel approaches for clustering patients based on their tumor immune profile, and discuss the latest progress on utilization of radiomics for immune profiling of glioma based on current literature.
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.