ArticleRadiology2015
Pseudoprogression in Patients with Glioblastoma: Assessment by Using Volume-weighted Voxel-based Multiparametric Clustering of MR Imaging Data in an Independent Test Set.
Article in Radiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02613988 (Early Response Assessment Using on 3T Advanced MR Imaging as Predictor of Long-term Treatment Response in Newly Diagnosed Glioblastomas), which is not on this map. Cited by 35 papers, 3 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.
Early Response Assessment Using on 3T Advanced MR Imaging as Predictor of Long-term Treatment Response in Newly Diagnosed Glioblastomas
Who cites it
35 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Discriminators of pseudoprogression and true progression in high-grade gliomas: A systematic review and meta-analysis.Scientific reports · 2022Pooled it
- Pooled it
- MR perfusion-weighted imaging in the evaluation of high-grade gliomas after treatment: a systematic review and meta-analysis.Neuro-oncology · 2017Pooled 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
- Intratumoral and peritumoral post-irradiation changes, but not viable tumor tissue, may respond to bevacizumab in previously irradiated meningiomas.Radiation oncology (London, England) · 2015Trial
- CT-based quantification of spatiotemporal heterogeneity for predicting response to neoadjuvant chemotherapy in locally advanced gastric cancer.BMC medical imaging · 2025Article
- Review
- Assessment of imaging risks for recurrence after stereotactic radiosurgery for brain metastases (IRRaS-BM).BMC cancer · 2024Observational
- Imaging Genomics of Glioma Revisited: Analytic Methods to Understand Spatial and Temporal Heterogeneity.AJNR. American journal of neuroradiology · 2024Review
- Advances in Neuro-Oncological Imaging: An Update on Diagnostic Approach to Brain Tumors.Cancers · 2024Review
- Perfusion magnetic resonance imaging in the differentiation between glioma recurrence and pseudoprogression: a systematic review, meta-analysis and meta-regression.Quantitative imaging in medicine and surgery · 2022Article
- Tumor Progression and Treatment-Related Changes: Radiological Diagnosis Challenges for the Evaluation of Post Treated Glioma.Cancers · 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
- Longitudinal structural and perfusion MRI enhanced by machine learning outperforms standalone modalities and radiological expertise in high-grade glioma surveillance.Neuroradiology · 2021Article
- Review
- Radiomics and Deep Learning from Research to Clinical Workflow: Neuro-Oncologic Imaging.Korean journal of radiology · 2020Review
- Machine learning and glioma imaging biomarkers.Clinical radiology · 2020Review
- Radiomics in peritumoral non-enhancing regions: fractional anisotropy and cerebral blood volume improve prediction of local progression and overall survival in patients with glioblastoma.Neuroradiology · 2019Article
- Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients.Neuro-oncology · 2019Article
- Differentiating between Glioblastoma and Primary CNS Lymphoma Using Combined Whole-tumor Histogram Analysis of the Normalized Cerebral Blood Volume and the Apparent Diffusion Coefficient.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo validate a volume-weighted voxel-based multiparametric clustering (VVMC) method for magnetic resonance imaging data that is designed to differentiate between pseudoprogression and early tumor progression (ETP) in patients with glioblastoma in an independent test set. MATERIALS AND
methodsThis retrospective study was approved by the local institutional review board, with waiver of the need to obtain informed consent. The study patients were grouped chronologically into a training set (108 patients) and a test set (54 patients). The reference standard was pathologic findings or subsequent clinical-radiologic study results. By using the optimal cutoff determined in the training set, the diagnostic performance of VVMC was subsequently tested in the test set and was compared with that of single-parameter measurements (apparent diffusion coefficient [ADC], normalized cerebral blood volume [nCBV], and initial area under the time-signal intensity curve).
resultsInterreader agreement was highest for VVMC (intraclass correlation coefficient, 0.87-0.89). Receiver operating characteristic curve analysis revealed that VVMC performed the best as a classifier, although statistical significance was not demonstrated with respect to the nCBV in the training set. In the test set, the diagnostic accuracy of VVMC was higher than that of any single-parameter measurements, but this trend reached significance only for the ADC. When the entire population was considered, VVMC had significantly better diagnostic accuracy than did any single parameter (P = .003-.046 for reader 1; P = .002-.016 for reader 2). Results of fivefold cross validation confirmed the trends in both the training set and the test set.
conclusionVVMC is a superior and more reproducible imaging biomarker than single-parameter measurements for differentiating between pseudoprogression and ETP in patients with glioblastoma. Online supplemental material is available for this article.
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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.