ArticlePloS one2021
Magnetic resonance radiomics features and prognosticators in different molecular subtypes of pediatric Medulloblastoma.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Predicting molecular subtypes of pediatric medulloblastoma using MRI-based artificial intelligence: A systematic review and meta-analysis.Neuroradiology · 2025Pooled it
- Application of radiomics for diagnosis, subtyping, and prognostication of medulloblastomas: a systematic review.Neurosurgical review · 2024Pooled it
- Artificial intelligence in paediatric neuroradiology: current landscape, challenges, and future directions.Pediatric radiology · 2026Review
- A Radiomics-Driven Model to Distinguish Between Clinically Similar Myxopapillary Ependymomas and Lumbosacral Schwannomas.Neurosurgery practice · 2026Article
- Machine learning based on radiomics for discriminating sellar region langerhans cell histiocytosis from germ cell tumors.Frontiers in pediatrics · 2026Article
- -New frontiers in domain-inspired radiomics and radiogenomics: increasing role of molecular diagnostics in CNS tumor classification and grading following WHO CNS-5 updates.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- Radiomic- and dosiomic-based clustering development for radio-induced neurotoxicity in pediatric medulloblastoma.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2024Article
- Article
- Opportunities and Advances in Radiomics and Radiogenomics for Pediatric Medulloblastoma Tumors.Diagnostics (Basel, Switzerland) · 2023Review
- Promoting the application of pediatric radiomics via an integrated medical engineering approach.Cancer innovation · 2023Review
- Current role of machine learning and radiogenomics in precision neuro-oncology.Exploration of targeted anti-tumor therapy · 2023Review
- Preoperative prediction of sonic hedgehog and group 4 molecular subtypes of pediatric medulloblastoma based on radiomics of multiparametric MRI combined with clinical parameters.Frontiers in neuroscience · 2023Article
- Machine-learning approach to predict molecular subgroups of medulloblastoma using multiparametric MRI-based tumor radiomics.The British journal of radiology · 2022Article
- Imaging diagnosis and treatment selection for brain tumors in the era of molecular therapeutics.Cancer imaging : the official publication of the International Cancer Imaging Society · 2022Review
- MR Imaging of Pediatric Brain Tumors.Diagnostics (Basel, Switzerland) · 2022Review
- Case report: Somatic mutations in microtubule dynamics-associated genes in patients with WNT-medulloblastoma tumors.Frontiers in oncology · 2022Article
- Radiomic-based prognostic score for survival risk-stratification in pediatric medulloblastoma tumors: A multi-institutional study.Neuro-oncology advancesArticle
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10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeMedulloblastoma (MB) is a highly malignant pediatric brain tumor. In the latest classification, medulloblastoma is divided into four distinct groups: wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4. We analyzed the magnetic resonance imaging radiomics features to find the imaging surrogates of the 4 molecular subgroups of MB. MATERIAL AND
methodsFrozen tissue, imaging data, and clinical data of 38 patients with medulloblastoma were included from Taipei Medical University Hospital and Taipei Veterans General Hospital. Molecular clustering was performed based on the gene expression level of 22 subgroup-specific signature genes. A total 253 magnetic resonance imaging radiomic features were generated from each subject for comparison between different molecular subgroups.
resultsOur cohort consisted of 7 (18.4%) patients with WNT medulloblastoma, 12 (31.6%) with SHH tumor, 8 (21.1%) with Group 3 tumor, and 11 (28.9%) with Group 4 tumor. 8 radiomics gray-level co-occurrence matrix texture (GLCM) features were significantly different between 4 molecular subgroups of MB. In addition, for tumors with higher values in a gray-level run length matrix feature-Short Run Low Gray-Level Emphasis, patients have shorter survival times than patients with low values of this feature (p = 0.04). The receiver operating characteristic analysis revealed optimal performance of the preliminary prediction model based on GLCM features for predicting WNT, Group 3, and Group 4 MB (area under the curve = 0.82, 0.72, and 0.78, respectively).
conclusionThe preliminary result revealed that 8 contrast-enhanced T1-weighted imaging texture features were significantly different between 4 molecular subgroups of MB. Together with the prediction models, the radiomics features may provide suggestions for stratifying patients with MB into different risk groups.
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