ArticleEuropean journal of nuclear medicine and molecular imaging2020
Individualized discrimination of tumor recurrence from radiation necrosis in glioma patients using an integrated radiomics-based model.
Article in European journal of nuclear medicine and molecular imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 4 of them syntheses that pooled it.
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Who cites it
47 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Radiomics-based artificial intelligence models in brain tumors: A systematic review and meta-analysis of diagnostic performance.Neuroradiology · 2026Pooled it
- Radiomics for differentiating radiation-induced brain injury from recurrence in gliomas: systematic review, meta-analysis, and methodological quality evaluation using METRICS and RQS.European radiology · 2025Pooled it
- Machine learning imaging applications in the differentiation of true tumour progression from treatment-related effects in brain tumours: A systematic review and meta-analysis.Journal of medical imaging and radiation oncology · 2022Pooled 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
- Radiomics in glioblastoma recurrence: advances in prediction, localization, and differentiation from treatment-related effects.Journal of translational medicine · 2026Review
- PET-based radiomic analysis in multicentre lung cancer study and impact of feature domain harmonization.Physical and engineering sciences in medicine · 2025Article
- Opportunities and challenges in application of VASARI features in neuro-oncology.Neuro-oncology practice · 2025Review
- Radiomics in glioma: emerging trends and challenges.Annals of clinical and translational neurology · 2025Review
- Preliminary investigation on predicting postoperative glioma recurrences based on a multiparametric radiomics model.Frontiers in oncology · 2025Article
- PET imaging of gliomas: Status quo and quo vadis?Neuro-oncology · 2024Review
- Preclinical Implementation of matRadiomics: A Case Study for Early Malformation Prediction in Zebrafish Model.Journal of imaging · 2024Article
- Multi-parameter MRI radiomics model in predicting postoperative progressive cerebral edema and hemorrhage after resection of meningioma.Cancer imaging : the official publication of the International Cancer Imaging Society · 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
- Development and validation of clinical-radiomics analysis for preoperative prediction of IDH mutation status and WHO grade in diffuse gliomas: a consecutive L-[methyl-11C] methionine cohort study with two PET scanners.European journal of nuclear medicine and molecular imaging · 2024Article
- Artificial intelligence-based MRI radiomics and radiogenomics in glioma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- 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
- T2-FLAIR imaging-based radiomic features for predicting early postoperative recurrence of grade II gliomas.Future oncology (London, England) · 2024Article
- Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT.Cancer management and research · 2024Review
- Individualized discrimination of tumor progression from treatment-related changes in different types of adult-type diffuse gliomas using [Journal of neuro-oncology · 2023Article
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10 authors.
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Abstract
purposeTo develop and validate an integrated model for discriminating tumor recurrence from radiation necrosis in glioma patients.
methodsData from 160 pathologically confirmed glioma patients were analyzed. The diagnostic model was developed in a primary cohort (n = 112). Textural features were extracted from postoperative
resultsThe integrated model consisting of 15 selected features was significantly associated with postoperative tumor recurrence (p < 0.001 for both primary and validation cohorts). Predictors contained in the individualized diagnosis model included the radiomics signature, the mean of tumor-background ratio (TBR) of
conclusionsOur developed model could be used to assist the postoperative individualized diagnosis of tumor recurrence in patients with gliomas.
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