ReviewNeuro-oncology2013
Differentiating tumor recurrence from treatment necrosis: a review of neuro-oncologic imaging strategies.
Review in Neuro-oncology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03068520 (Imaging After Stereotactic Radiosurgery for Brain Metastases or Primary Tumor Can Hybrid PET-MRI Differentiate Between Radiation Effects and Disease ?), which is not on this map. Cited by 199 papers, 11 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.
Imaging After Stereotactic Radiosurgery for Brain Metastases or Primary Tumor Can Hybrid PET-MRI Differentiate Between Radiation Effects and Disease ?
Who cites it
199 citing papers in PubMed, 11 syntheses or guidelines pooled it.
- Magnetic resonance spectroscopy (MRS) for differentiating tumour recurrence from radiation necrosis in treated intra-axial brain tumours.The Cochrane database of systematic reviews · 2026Pooled it
- Performance of amide proton transfer imaging to differentiate true progression from therapy-related changes in gliomas and metastases.European radiology · 2025Pooled it
- Differentiating radiation necrosis from tumor recurrence: a systematic review and diagnostic meta-analysis comparing imaging modalities.Journal of neuro-oncology · 2023Pooled it
- Comparing [18F]FET PET and [18F]FDOPA PET for glioma recurrence diagnosis: a systematic review and meta-analysis.Frontiers in oncology · 2023Pooled it
- Differentiation between glioma recurrence and treatment effects using amide proton transfer imaging: A mini-Bayesian bivariate meta-analysis.Frontiers in oncology · 2022Pooled it
- Imaging Biomarkers of Glioblastoma Treatment Response: A Systematic Review and Meta-Analysis of Recent Machine Learning Studies.Frontiers in oncology · 2022Pooled it
- Diagnostic Accuracy of PET for Differentiating True Glioma Progression From Post Treatment-Related Changes: A Systematic Review and Meta-Analysis.Frontiers in neurology · 2021Pooled it
- Diagnostic Accuracy of Amino Acid and FDG-PET in Differentiating Brain Metastasis Recurrence from Radionecrosis after Radiotherapy: A Systematic Review and Meta-Analysis.AJNR. American journal of 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
- MR perfusion-weighted imaging in the evaluation of high-grade gliomas after treatment: a systematic review and meta-analysis.Neuro-oncology · 2017Pooled it
- Differentiating Radiation-Induced Necrosis from Recurrent Brain Tumor Using MR Perfusion and Spectroscopy: A Meta-Analysis.PloS one · 2016Pooled it
- Trial
- Treatment-associated imaging changes in newly diagnosed MGMT promoter-methylated glioblastoma undergoing chemoradiation with or without cilengitide.Neuro-oncology · 2024Trial
- Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A new radiomics descriptor.Scientific reports · 2016Trial
- Reirradiation of recurrent brain tumors: a review by the ESTRO CNS Focus Group.Clinical and translational radiation oncology · 2027Review
- Enhanced Glioblastoma Detection ThroughMagnetic resonance in medicine · 2026Article
- Article
- Distinguishing true progression from treatment effects in glioblastoma: a practical, evidence-graded imaging framework for the multidisciplinary team.Journal of neuro-oncology · 2026Review
- Spatially identifying regions of tumor recurrence in patients with suspected recurrent glioma using physiologic MRI and machine learning.NPJ digital medicine · 2026Article
- Post-Treatment MRI Features on First Follow-Up Imaging in Diffuse Gliomas After Near-Total Resection: A Real-World Exploratory Cohort Study.Medicina (Kaunas, Lithuania) · 2026Article
139 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Differentiating treatment-induced necrosis from tumor recurrence is a central challenge in neuro-oncology. These 2 very different outcomes after brain tumor treatment often appear similarly on routine follow-up imaging studies. They may even manifest with similar clinical symptoms, further confounding an already difficult process for physicians attempting to characterize a new contrast-enhancing lesion appearing on a patient's follow-up imaging. Distinguishing treatment necrosis from tumor recurrence is crucial for diagnosis and treatment planning, and therefore, much effort has been put forth to develop noninvasive methods to differentiate between these disparate outcomes. In this article, we review the latest developments and key findings from research studies exploring the efficacy of structural and functional imaging modalities for differentiating treatment necrosis from tumor recurrence. We discuss the possibility of computational approaches to investigate the usefulness of fine-grained imaging characteristics that are difficult to observe through visual inspection of images. We also propose a flexible treatment-planning algorithm that incorporates advanced functional imaging techniques when indicated by the patient's routine follow-up images and clinical condition.
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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.