SynthesisMedicine2020
Meta-analysis of the diagnostic performance of diffusion magnetic resonance imaging with apparent diffusion coefficient measurements for differentiating glioma recurrence from pseudoprogression.
Synthesis in Medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Umbrella review and network meta-analysis of diagnostic imaging test accuracy studies in Differentiating between brain tumor progression versus pseudoprogression and radionecrosis.Journal of neuro-oncology · 2024Pooled it
- Imaging in Neuro-oncology.Seminars in neurology · 2025Review
- Deep Learning Segmentation of Infiltrative and Enhancing Cellular Tumor at Pre- and Posttreatment Multishell Diffusion MRI of Glioblastoma.Radiology. Artificial intelligence · 2024Article
- Article
- Undetected pseudoprogressions in the CeTeG/NOA-09 trial: hints from postprogression survival and MRI analyses.Journal of neuro-oncology · 2023Article
- Liquid Biopsy in Glioblastoma.Cancers · 2022Review
- Conventional and Advanced Imaging Techniques in Post-treatment Glioma Imaging.Frontiers in radiology · 2022Review
- High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 1: Perfusion and Diffusion Techniques.Frontiers in oncology · 2022Review
- Effects of Multi-Shell Free Water Correction on Glioma Characterization.Diagnostics (Basel, Switzerland) · 2021Article
- ADC-Based Stratification of Molecular Glioma Subtypes Using High b-Value Diffusion-Weighted Imaging.Journal of clinical medicine · 2021Article
- MRI biomarkers in neuro-oncology.Nature reviews. Neurology · 2021Review
- Advanced Imaging Techniques for Differentiating Pseudoprogression and Tumor Recurrence After Immunotherapy for Glioblastoma.Frontiers in immunology · 2021Review
- Review
- Multiparametric radiomic tissue signature and machine learning for distinguishing radiation necrosis from tumor progression after stereotactic radiosurgery.Neuro-oncology advancesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe accurate differentiation of glioma recurrence from pseudoprogression (PSP) after therapy remains a considerable clinical challenge. Several studies have shown that diffusion magnetic resonance imaging (MRI) has potential value in distinguishing these 2 outcomes. The current meta-analysis examined the diagnostic accuracy of diffusion MRI with the apparent diffusion coefficient (ADC) in the differentiation of glioma recurrence from PSP.
methodPubMed, Embase, Cochrane Library, and Chinese Biomedical databases were reviewed to identify studies that fulfilled our inclusion/exclusion criteria and were published on or before May 5, 2019. Threshold effects; heterogeneity; pooled sensitivity (SENS), specificity, positive likelihood ratio, and negative likelihood ratio; and diagnostic odds ratio were calculated. The overall diagnostic usefulness of diffusion MRI-derived ADC values was assessed by calculating the area under the curve (AUC) following summary receiver operating characteristic (SROC) analysis.
resultsSix eligible studies examined a total of 214 patients. Calculation of pooled values indicated the SENS was 0.95 (95% confidence interval [CI] = 0.89-0.98), specificity was 0.83 (95% CI = 0.72-0.91), positive likelihood ratio was 4.82 (95% CI = 2.93-7.93), negative likelihood ratio was 0.08 (95% CI = 0.04-0.17), and diagnostic odds ratio was 59.63 (95% CI = 22.63-157.37). The SROC AUC was 0.9322. Publication bias was not significant, and SENS analysis indicated the results were relatively stable.
conclusionsOur meta-analysis indicated that diffusion MRI with quantitative ADC is an effective approach for differentiation of glioma recurrence from PSP, and can be used as an auxiliary tool to diagnose glioma progression.
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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.