ArticleFrontiers in oncology2021
Radiomic Analysis to Predict Outcome in Recurrent Glioblastoma Based on Multi-Center MR Imaging From the Prospective DIRECTOR Trial.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Prediction of MGMT methylation status in glioblastoma patients based on radiomics feature extracted from intratumoral and peritumoral MRI imaging.Scientific reports · 2025Article
- Development of A Radiomic Model forInternational journal of molecular sciences · 2023Article
- Article
- The peritumoral brain zone in glioblastoma: where we are and where we are going.Journal of neuroscience research · 2023Review
- Article
- Radiomic and Volumetric Measurements as Clinical Trial Endpoints-A Comprehensive Review.Cancers · 2022Review
- Review
- Brain Tumor Imaging: Applications of Artificial Intelligence.Seminars in ultrasound, CT, and MR · 2022Article
- Robustness of radiomic features in magnetic resonance imaging for patients with glioblastoma: Multi-center study.Physics and imaging in radiation oncology · 2022Article
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBased on promising results from radiomic approaches to predict
methodsPre-treatment MR-imaging data of 69 patients enrolled into the DIRECTOR trial in recurrent glioblastoma served as a training cohort, and 49 independent patients formed an external validation cohort. Contrast-enhancing tumor and peritumoral volumes were segmented on MR images. 180 radiomic features were extracted after application of two MR intensity normalization techniques: fixed number of bins and linear rescaling. Radiomic feature selection was performed
resultsWe established and validated a radiomic model to predict
conclusionsA radiomic model for prediction of
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Registered trials
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