ReviewFrontiers in neurology2022
Preoperative Diagnosis and Molecular Characterization of Gliomas With Liquid Biopsy and Radiogenomics.
Review in Frontiers in neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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.
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
22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 33 citations in OpenAlex.
- Is There a Role for Machine Learning in Liquid Biopsy for Brain Tumors? A Systematic Review.International journal of molecular sciences · 2023Pooled it
- Quantitative study of bioinformatics analysis on glioma: a bibliometric analysis.Frontiers in oncology · 2023Pooled it
- Extracellular Vesicles in the Biology and Liquid Biopsy Diagnostics of Pediatric High-Grade Glioma - Emerging Findings and Opportunities.Journal of extracellular biology · 2026Review
- Amide proton transfer and arterial spin labeling for non-invasive molecular stratification of glioma: a multi-dataset imaging biomarker study.Neuroradiology · 2026Article
- Review
- Radiomics and clinical data predict pseudoprogression after radiotherapy in high-grade glioma.Frontiers in oncology · 2026Article
- Molecular Biomarkers of Glioma.Biomedicines · 2025Review
- Liquid biopsy for the detection of H3K27m in patients with brainstem tumors.Neurosurgical review · 2025Article
- Liquid Biopsy-Derived Tumor Biomarkers for Clinical Applications in Glioblastoma.Biomolecules · 2025Review
- Robust radiomics: a review of guidelines for radiomics in medical imaging.Frontiers in radiology · 2025Review
- MEX3A is a diagnostic, independent prognostic biomarker and a promising therapeutic target in glioblastoma.Frontiers in oncology · 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
- Differentiating IDH-mutant astrocytomas and 1p19q-codeleted oligodendrogliomas using DSC-PWI: high performance through cerebral blood volume and percentage of signal recovery percentiles.European radiology · 2024Article
- DSC-PWI presurgical differentiation of grade 4 astrocytoma and glioblastoma in young adults: rCBV percentile analysis across enhancing and non-enhancing regions.Neuroradiology · 2024Article
- Auto-segmentation of Adult-Type Diffuse Gliomas: Comparison of Transfer Learning-Based Convolutional Neural Network Model vs. Radiologists.Journal of imaging informatics in medicine · 2024Article
- Computed tomography radiogenomics: A potential tool for prediction of molecular subtypes in gastric stromal tumor.World journal of gastrointestinal oncology · 2024Article
- The potential of miRNA-based approaches in glioblastoma: An update in current advances and future perspectives.Current research in pharmacology and drug discovery · 2024Review
- Review
- Differential metabolic alterations in IDH1 mutant vs. wildtype glioma cells promote epileptogenesis through distinctive mechanisms.Frontiers in cellular neuroscience · 2023Review
- Combination of multi-modal MRI radiomics and liquid biopsy technique for preoperatively non-invasive diagnosis of glioma based on deep learning: protocol for a double-center, ambispective, diagnostical observational study.Frontiers in molecular neuroscience · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gliomas are a heterogenous group of central nervous system tumors with different outcomes and different therapeutic needs. Glioblastoma, the most common subtype in adults, has a very poor prognosis and disabling consequences. The World Health Organization (WHO) classification specifies that the typing and grading of gliomas should include molecular markers. The molecular characterization of gliomas has implications for prognosis, treatment planning, and prediction of treatment response. At present, gliomas are diagnosed via tumor resection or biopsy, which are always invasive and frequently risky methods. In recent years, however, substantial advances have been made in developing different methods for the molecular characterization of tumors through the analysis of products shed in body fluids. Known as liquid biopsies, these analyses can potentially provide diagnostic and prognostic information, guidance on choice of treatment, and real-time information on tumor status. In addition, magnetic resonance imaging (MRI) is another good source of tumor data; radiomics and radiogenomics can link the imaging phenotypes to gene expression patterns and provide insights to tumor biology and underlying molecular signatures. Machine and deep learning and computational techniques can also use quantitative imaging features to non-invasively detect genetic mutations. The key molecular information obtained with liquid biopsies and radiogenomics can be useful not only in the diagnosis of gliomas but can also help predict response to specific treatments and provide guidelines for personalized medicine. In this article, we review the available data on the molecular characterization of gliomas using the non-invasive methods of liquid biopsy and MRI and suggest that these tools could be used in the future for the preoperative diagnosis of gliomas.
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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.