Trial reportScientific reports2019
11C-methionine-PET for differentiating recurrent brain tumor from radiation necrosis: radiomics approach with random forest classifier.
Trial report in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 2 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
53 citing papers in PubMed, 2 syntheses or guidelines pooled it, 97 citations in OpenAlex.
- Radiomics-based artificial intelligence models in brain tumors: A systematic review and meta-analysis of diagnostic performance.Neuroradiology · 2026Pooled it
- Diagnostic Accuracy of PET for Differentiating True Glioma Progression From Post Treatment-Related Changes: A Systematic Review and Meta-Analysis.Frontiers in neurology · 2021Pooled it
- Phase 1 dose-escalation trial combining sulfasalazine and stereotactic radiosurgery in patients with recurrent glioblastoma.Redox biology · 2026Article
- PET-based Radiomics Analysis for Predicting Prognosis and Differentiation Treatment-Related Changes in Glioma: A Systematic Review.Journal of biomedical physics & engineering · 2026Article
- Radiomics-based signature of breast cancer on preoperative contrast-enhanced MRI to predict axillary metastasis.Future oncology (London, England) · 2026Article
- Beyond morphology: imaging the glioblastoma microenvironment in the era of quantitative neuro-oncology.Frontiers in medicine · 2026Review
- Article
- Review
- Artificial Intelligence in the Diagnosis and Treatment of Brain Gliomas.Biomedicines · 2025Review
- Stereotactic radiosurgery for patients with brain metastases: current principles, expanding indications and opportunities for multidisciplinary care.Nature reviews. Clinical oncology · 2025Review
- Cerebral radiation necrosis successfully treated with high-dose bevacizumab.Respiratory medicine case reports · 2025Article
- PET imaging of gliomas: Status quo and quo vadis?Neuro-oncology · 2024Review
- Hybrid Positron Emission Tomography and Magnetic Resonance Imaging Guided Microsurgical Management of Glial Tumors: Case Series and Review of the Literature.Diagnostics (Basel, Switzerland) · 2024Article
- Recurrence Patterns after Radiotherapy for Glioblastoma with [(11)C]methionine Positron Emission Tomography-Guided Irradiation for Target Volume Optimization.Diagnostics (Basel, Switzerland) · 2024Article
- A comparison study of dynamic [Journal of cancer research and clinical oncology · 2024Article
- Application of PET imaging delta radiomics for predicting progression-free survival in rare high-grade glioma.Scientific reports · 2024Article
- Artificial Intelligence Analysis Using MRI and PET Imaging in Gliomas: A Narrative Review.Cancers · 2024Review
- Resection of positive tissue on methionine-PET is associated with improved survival in glioblastomas.Brain and behavior · 2023Article
- Review
- The usefulness of machine-learning-based evaluation of clinical and pretreatmentThe British journal of radiology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Differentiating recurrent brain tumor from radiation necrosis is often difficult. This study aims to investigate the efficacy of 11C-methionine (MET)-PET radiomics for distinguishing recurrent brain tumor from radiation necrosis, as compared with conventional tumor-to-normal cortex (T/N) ratio evaluation. We enrolled 41 patients with metastatic brain tumor or glioma treated using radiation therapy who underwent MET-PET. The area with a standardized uptake value > 1.3 times that of the normal brain cortex was contoured. Forty-two PET features were extracted and used in a random forest classifier and the diagnostic performance was evaluated using a 10-fold cross-validation scheme. Gini index was measured to identify relevant PET parameters for classification. The reference standard was surgical histopathological analysis or more than 6 months of follow-up with MRI. Forty-four lesions were used for the analysis. Thirty-three and 11 lesions were confirmed as recurrent brain tumor and radiation necrosis, respectively. Radiomics and T/N ratio evaluation showed sensitivities of 90.1% and 60.6%, and specificities of 93.9% and 72.7% with areas under the curve of 0.98 and 0.73, respectively. Gray level co-occurrence matrix dissimilarity was the most pertinent feature for diagnosis. MET-PET radiomics yielded excellent outcome for differentiating recurrent brain tumor from radiation necrosis, which outperformed T/N ratio evaluation.
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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.