ArticleFrontiers in neuroscience2016
Classifying Glioblastoma Multiforme Follow-Up Progressive vs. Responsive Forms Using Multi-Parametric MRI Features.
Article in Frontiers in neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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.
- Comparative study of radiomics features between ASL and DSC perfusion-weighted imaging in distinguishing radiation-induced brain injury from glioma recurrence.Neuroradiology · 2026Article
- Article
- Radiomics and visual analysis for predicting success of transplantation of heterotopic glioblastoma in mice with MRI.Journal of neuro-oncology · 2024Article
- Molecular MRI-Based Monitoring of Cancer Immunotherapy Treatment Response.International journal of molecular sciences · 2023Review
- Radiomics analysis of amide proton transfer-weighted and structural MR images for treatment response assessment in malignant gliomas.NMR in biomedicine · 2023Article
- Machine Learning for Prediction of Recurrence in Parasagittal and Parafalcine Meningiomas: Combined Clinical and MRI Texture Features.Journal of personalized medicine · 2022Article
- Differentiation of Low-Grade Astrocytoma From Anaplastic Astrocytoma Using Radiomics-Based Machine Learning Techniques.Frontiers in oncology · 2021Article
- Machine learning and glioma imaging biomarkers.Clinical radiology · 2020Review
- Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients.Neuro-oncology · 2019Article
- Glioblastoma and Anaplastic Astrocytoma: Differentiation Using MRI Texture Analysis.Frontiers in oncology · 2019Article
- Current and Future Imaging Methods for Evaluating Response to Immunotherapy in Neuro-Oncology.Theranostics · 2019Review
- The Diagnostic Value of MRI-Based Texture Analysis in Discrimination of Tumors Located in Posterior Fossa: A Preliminary Study.Frontiers in neuroscience · 2019Article
- Characterization of active and infiltrative tumorous subregions from normal tissue in brain gliomas using multiparametric MRI.Journal of magnetic resonance imaging : JMRI · 2018Article
- Evaluating the Gray Level Co-Occurrence Matrix-Based Texture Features of Magnetic Resonance Images for Glioblastoma Multiform Patients' Treatment Response Assessment.Journal of medical signals and sensorsArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
Indexed as
Identifiers
What OpenQuestion holds
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.