ReviewCancers2021
Advancements in Neuroimaging to Unravel Biological and Molecular Features of Brain Tumors.
Review in Cancers, 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.
- Predicting IDH and ATRX mutations in gliomas from radiomic features with machine learning: a systematic review and meta-analysis.Frontiers in radiology · 2024Pooled it
- Multidisciplinary recommendations for routine follow-up of isocitrate dehydrogenase‑mutant glioma in 2026: Adapting clinical practice to the molecular era.Neuro-oncology practice · 2026Review
- Microstructure imaging in patients undergoing evaluation for epilepsy surgery or low-grade glioma: Clinical utility of a novel diffusion MRI method.Epilepsia open · 2026Article
- Multimodal radiomics fusion for predicting postoperative recurrence in NSCLC patients.Journal of cancer research and clinical oncology · 2025Article
- Diffusion and contrast-enhancement MRI phenotypes associated with immune checkpoint inhibitor exposure and with immune cell infiltration in brain metastases.Neuro-oncology · 2025Article
- From pixels to prognosis: leveraging radiomics and machine learning to predict IDH1 genotype in gliomas.Neurosurgical review · 2025Article
- "Synthetic" DSC Perfusion MRI with Adjustable Acquisition Parameters in Brain Tumors Using Dynamic Spin-and-Gradient-Echo Echoplanar Imaging.AJNR. American journal of neuroradiology · 2025Article
- Detecting glioblastoma infiltration beyond conventional imaging tumour margins using MTE-NODDI.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Sub-MicroliterJournal of neurochemistry · 2025Article
- Inferring the genetic relationships between unsupervised deep learning-derived imaging phenotypes and glioblastoma through multi-omics approaches.Briefings in bioinformatics · 2024Article
- Evolution of Molecular Biomarkers and Precision Molecular Therapeutic Strategies in Glioblastoma.Cancers · 2024Review
- Review
- Small pretreatment lesion size and high sphericity as favorable prognostic factors after laser interstitial thermal therapy in brain metastases.Journal of neurosurgery · 2024Article
- Neurochemical Differences between 1p/19q Codeleted and Noncodeleted IDH-mutant Gliomas by in Vivo MR Spectroscopy.Radiology · 2023Observational
- Article
- Imaging biomarkers for clinical applications in neuro-oncology: current status and future perspectives.Biomarker research · 2023Review
- Standardized brain tumor imaging protocols for clinical trials: current recommendations and tips for integration.Frontiers in radiology · 2023Review
- Advanced intraoperative MRI in pediatric brain tumor surgery.Frontiers in physiology · 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
- Beyond Imaging and Genetic Signature in Glioblastoma: Radiogenomic Holistic Approach in Neuro-Oncology.Biomedicines · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, the clinical assessment of primary brain tumors has been increasingly dependent on advanced magnetic resonance imaging (MRI) techniques in order to infer tumor pathophysiological characteristics, such as hemodynamics, metabolism, and microstructure. Quantitative radiomic data extracted from advanced MRI have risen as potential in vivo noninvasive biomarkers for predicting tumor grades and molecular subtypes, opening the era of "molecular imaging" and radiogenomics. This review presents the most relevant advancements in quantitative neuroimaging of advanced MRI techniques, by means of radiomics analysis, applied to primary brain tumors, including lower-grade glioma and glioblastoma, with a special focus on peculiar oncologic entities of current interest. Novel findings from diffusion MRI (dMRI), perfusion-weighted imaging (PWI), and MR spectroscopy (MRS) are hereby sifted in order to evaluate the role of quantitative imaging in neuro-oncology as a tool for predicting molecular profiles, stratifying prognosis, and characterizing tumor tissue microenvironments. Furthermore, innovative technological approaches are briefly addressed, including artificial intelligence contributions and ultra-high-field imaging new techniques. Lastly, after providing an overview of the advancements, we illustrate current clinical applications and future perspectives.
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.