ArticleNeuroImage. Clinical2018
Combined FET PET/MRI radiomics differentiates radiation injury from recurrent brain metastasis.
Article in NeuroImage. Clinical, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 3 of them syntheses that pooled it.
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The trial behind it
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Who cites it
78 citing papers in PubMed, 3 syntheses or guidelines pooled it, 134 citations in OpenAlex.
- Radiomics-based artificial intelligence models in brain tumors: A systematic review and meta-analysis of diagnostic performance.Neuroradiology · 2026Pooled it
- Umbrella review and network meta-analysis of diagnostic imaging test accuracy studies in Differentiating between brain tumor progression versus pseudoprogression and radionecrosis.Journal of neuro-oncology · 2024Pooled it
- DEGRO practical guideline for central nervous system radiation necrosis part 1: classification and a multistep approach for diagnosis.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2022Guideline
- Radiomics-based Differentiation of Recurrent Brain Metastases from Treatment Effects: A Multi-Institutional Comparative Study with Advanced Imaging.Radiology. Imaging cancer · 2026Article
- AI differentiates radionecrosis from true progression in brain metastasis upon stereotactic radiosurgery: Analysis of 124 histologically assessed lesions.Neuro-oncology · 2025Article
- Optimizing Stereotactic Intracranial Neoplasm Treatment: A Systematic Review of PET Integration with Gamma Knife Radiosurgery.Diseases (Basel, Switzerland) · 2025Review
- Brain Tumor Recurrence vs. Radiation Necrosis Classification and Patient Survivability Prediction.IEEE journal of biomedical and health informatics · 2024Article
- 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
- Prediction of treatment response after stereotactic radiosurgery of brain metastasis using deep learning and radiomics on longitudinal MRI data.Scientific reports · 2024Article
- Evaluating the Efficacy of Perfusion MRI and Conventional MRI in Distinguishing Recurrent Cerebral Metastasis from Brain Radiation Necrosis.Brain sciences · 2024Article
- Artificial intelligence-based MRI radiomics and radiogenomics in glioma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- Advances in Neuro-Oncological Imaging: An Update on Diagnostic Approach to Brain Tumors.Cancers · 2024Review
- Article
- Review
- Feasibility of radiomic feature harmonization for pooling of [Zeitschrift fur medizinische Physik · 2023Article
- Combination of pre-treatment dynamic [European journal of nuclear medicine and molecular imaging · 2023Article
- Bibliometric research on the developments of artificial intelligence in radiomics toward nervous system diseases.Frontiers in neurology · 2023Article
- Using domain knowledge for robust and generalizable deep learning-based CT-free PET attenuation and scatter correction.Nature communications · 2022Article
- Can Radiomics Provide Additional Information in [Cancers · 2022Article
- Static FET PET radiomics for the differentiation of treatment-related changes from glioma progression.Journal of neuro-oncology · 2022Article
18 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The aim of this study was to investigate the potential of combined textural feature analysis of contrast-enhanced MRI (CE-MRI) and static O-(2-[ Methods: Fifty-two patients with new or progressive contrast-enhancing brain lesions on MRI after radiotherapy (predominantly stereotactic radiosurgery) of brain metastases were additionally investigated using FET PET. Based on histology ( Results: For the differentiation between radiation injury and recurrence of brain metastasis, textural features extracted from CE-MRI had a diagnostic accuracy of 81% (sensitivity, 67%; specificity, 90%). FET PET textural features revealed a slightly higher diagnostic accuracy of 83% (sensitivity, 88%; specificity, 75%). However, the highest diagnostic accuracy was obtained when combining CE-MRI and FET PET features (accuracy, 89%; sensitivity, 85%; specificity, 96%). Conclusions: Our findings suggest that combined FET PET/CE-MRI radiomics using textural feature analysis offers a great potential to contribute significantly to the management of patients with brain metastases.
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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.