ReviewInsights into imaging2022
Diffusion imaging could aid to differentiate between glioma progression and treatment-related abnormalities: a meta-analysis.
Review in Insights into imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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.
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Who cites it
10 citing papers in PubMed.
- Independent histological validation of MR-derived radio-pathomic maps of tumor cell density using image-guided biopsies in human brain tumors.Journal of neuro-oncology · 2025Article
- Artificial Intelligence in the Diagnosis and Treatment of Brain Gliomas.Biomedicines · 2025Review
- Multi-site retrospective analysis of diffusion and perfusion magnetic resonance imaging correlates to glioma characteristics derived from radio-pathomic maps.Neuro-oncology · 2025Article
- Impact of glycemic control on brain microstructure in type 2 diabetes mellitus: insights from diffusion tensor imaging.BMC medical imaging · 2025Article
- The Predictive Value of ADC Values, Degree of Edema and the Systemic Immune-Inflammation Index for Early Postoperative Recurrence in High-Grade Gliomas.Journal of inflammation research · 2025Article
- Distinguishing glioblastoma progression from treatment-related changes using DTI directionality growth analysis.Neuroradiology · 2024Article
- Article
- Prediction of Myocardial Infarction Using a Combined Generative Adversarial Network Model and Feature-Enhanced Loss Function.Metabolites · 2024Article
- Article
- Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundIn a considerable subgroup of glioma patients treated with (chemo) radiation new lesions develop either representing tumor progression (TP) or treatment-related abnormalities (TRA). Quantitative diffusion imaging metrics such as the Apparent Diffusion Coefficient (ADC) and Fractional Anisotropy (FA) have been reported as potential metrics to noninvasively differentiate between these two phenomena. Variability in performance scores of these metrics and absence of a critical overview of the literature contribute to the lack of clinical implementation. This meta-analysis therefore critically reviewed the literature and meta-analyzed the performance scores.
methodsSystematic searching was carried out in PubMed, EMBASE and The Cochrane Library. Using predefined criteria, papers were reviewed. Diagnostic accuracy values of suitable papers were meta-analyzed quantitatively.
resultsOf 1252 identified papers, 10 ADC papers, totaling 414 patients, and 4 FA papers, with 154 patients were eligible for meta-analysis. Mean ADC values of the patients in the TP/TRA groups were 1.13 × 10
conclusionsQuantitative ADC and FA values could be useful for distinguishing TP from TRA on a meta-level. Further studies using serial imaging of individual patients are warranted to determine the role of diffusion imaging in glioma patients.
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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.