Trial reportNeuroradiology2013
Evaluation of dynamic contrast-enhanced T1-weighted perfusion MRI in the differentiation of tumor recurrence from radiation necrosis.
Trial report in Neuroradiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 4 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
59 citing papers in PubMed, 4 syntheses or guidelines pooled it, 109 citations in OpenAlex.
- Diagnostic accuracy of dynamic contrast-enhanced perfusion MRI in stratifying gliomas: A systematic review and meta-analysis.Cancer medicine · 2019Pooled it
- Incidence of Tumour Progression and Pseudoprogression in High-Grade Gliomas: a Systematic Review and Meta-Analysis.Clinical neuroradiology · 2018Pooled it
- Diagnostic accuracy of magnetic resonance imaging techniques for treatment response evaluation in patients with high-grade glioma, a systematic review and meta-analysis.European radiology · 2017Pooled it
- Assessment of blood-brain barrier disruption using dynamic contrast-enhanced MRI. A systematic review.NeuroImage. Clinical · 2014Pooled it
- Comparison of Dynamic Contrast-Enhancement Parameters between Gadobutrol and Gadoterate Meglumine in Posttreatment Glioma: A Prospective Intraindividual Study.AJNR. American journal of neuroradiology · 2020Trial
- Colour maps for representation of quantitative metrics in radiology: meaningful or misleading?Insights into imaging · 2026Review
- Downstream evaluation of synthetic AI-generated TFrontiers in radiology · 2026Article
- Glioblastoma at the crossroads: current understanding and future therapeutic horizons.Signal transduction and targeted therapy · 2025Review
- Comprehensive Review of the Utility of Dynamic Contrast-Enhanced MRI for the Diagnosis and Treatment Assessment of Spinal Benign and Malignant Osseous Disease.AJNR. American journal of neuroradiology · 2025Review
- The diagnostic value of advanced tracer kinetic models in evaluating high grade gliomas recurrence and treatment response using dynamic contrast-enhanced MRI.Frontiers in oncology · 2025Article
- Review
- Review of tracer kinetic models in evaluation of gliomas using dynamic contrast-enhanced imaging.Frontiers in oncology · 2024Review
- Article
- Diagnostic yield of simultaneous dynamic contrast-enhanced magnetic resonance perfusion measurements and [European journal of nuclear medicine and molecular imaging · 2022Article
- Volumetric Measurement of Relative CBV Using T1-Perfusion-Weighted MRI with High Temporal Resolution Compared with Traditional T2*-Perfusion-Weighted MRI in Postoperative Patients with High-Grade Gliomas.AJNR. American journal of neuroradiology · 2022Article
- Review
- New phase therapeutic pursuits for targeted drug delivery in glioblastoma multiforme.Exploration of targeted anti-tumor therapy · 2022Review
- Conventional and advanced imaging throughout the cycle of care of gliomas.Neurosurgical review · 2021Review
- Magnetic Resonance-Guided High-Intensity Focused Ultrasound Ablation of Uterine Fibroids-Efficiency Assessment with the Use of Dynamic Contrast-Enhanced Magnetic Resonance Imaging and the Potential Role of the Administration of Uterotonic Drugs.Diagnostics (Basel, Switzerland) · 2021Article
- Differentiation of recurrent diffuse glioma from treatment-induced change using amide proton transfer imaging: incremental value to diffusion and perfusion parameters.Neuroradiology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionTo investigate if perfusion measured with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can be used to differentiate radiation necrosis from tumor recurrence in patients with high-grade glioma.
methodsThe study was approved by the institutional review board and informed consent was obtained from all subjects. 19 patients were recruited following surgery and radiation therapy for glioma. Patients had contrast enhancing lesions, which during the standard MRI examination could not be exclusively determined as recurrence or radiation necrosis. DCE-MRI was used to measure cerebral blood volume (CBV), blood-brain barrier (BBB) permeability and cerebral blood flow (CBF). Subjects also underwent FDG-PET and lesions were classified as either metabolically active or inactive. Follow-up clinical MRI and lesion histology in case of additional tissue resection was used to determine whether lesions were regressing or progressing.
resultsFourteen enhancing lesions could be classified as progressing (11) or regressing (three). An empirical threshold of 2.0 ml/100 g for CBV allowed detection of regressing lesions with a sensitivity of 100 % and specificity of 100 %. FDG-PET and DCE-MRI agreed in classification of tumor status in 13 out of the 16 cases where an FDG-PET classification was obtained. In two of the remaining three patients, MRI follow-up and histology was available and both indicated that the DCE-MRI answer was correct.
conclusionCBV measurements using DCE-MRI may predict the status of contrast enhancing lesions and give results very similar to FDG-PET with regards to differentiation between tumor recurrence and radiation necrosis.
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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.