ArticleThe British journal of radiology2022
Machine-learning approach to predict molecular subgroups of medulloblastoma using multiparametric MRI-based tumor radiomics.
Article in The British journal of radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 3 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 3 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
- Current state of radiomics in pediatric neuro-oncology practice: a systematic review.Pediatric radiology · 2023Pooled it
- Dual-task deep learning model for prediction of medulloblastoma molecular subgroups with preoperative brain MRI.La Radiologia medica · 2026Article
- AI-Powered Histology for Molecular Profiling in Brain Tumors: Toward Smart Diagnostics from Tissue.Cancers · 2025Review
- Radiogenomic MRI biomarkers for noninvasive prediction of GPC3 expression and tumor microenvironment in hepatocellular carcinoma.Journal of translational medicine · 2025Article
- Radiomics in pediatric brain tumors: from images to insights.Discover oncology · 2025Review
- Developing a novel medulloblastoma diagnostic with miRNA biomarkers and machine learning.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2025Article
- Application of Radiomics in Predicting the Prognosis of Medulloblastoma in Children.Children (Basel, Switzerland) · 2025Article
- Revolutionizing precision oncology: the role of artificial intelligence in personalized pediatric cancer care.Frontiers in medicine · 2025Review
- Article
- Opportunities and Advances in Radiomics and Radiogenomics for Pediatric Medulloblastoma Tumors.Diagnostics (Basel, Switzerland) · 2023Review
- Article
- Computational approaches for evaluating morphological changes in the corneal stroma associated with decellularization.Frontiers in bioengineering and biotechnology · 2023Article
- 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
- Radiomic-based prognostic score for survival risk-stratification in pediatric medulloblastoma tumors: A multi-institutional study.Neuro-oncology advancesArticle
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12 authors.
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Abstract
objectiveImage-based prediction of molecular subgroups of Medulloblastoma (MB) has the potential to optimize and personalize therapy. The objective of the study is to distinguish between broad molecular subgroups of MB using MR-Texture analysis.
methodsThirty-eight MB patients treated between 2007 and 2020 were retrospectively analyzed. Texture analysis was performed on contrast enhanced T1(T1C) and
resultsA total of 174 and 170 images were obtained for analysis from the Axial T1C and T2W image datasets. One hundred and sixty-four MR based texture features were extracted. The best model was arrived at by using a combination of 30 GLCM and six shape features on T1C MR sequence. A 10-fold cross-validation demonstrated an AUC of 0.93, 0.9, 0.93, and 0.93 in predicting WNT, SHH, Group 3, and Group 4 MB subgroups, respectively.
conclusionRadiomic analysis of MR images in MB can predict molecular subgroups with acceptable degree of accuracy. The strategy needs further validation in an external dataset for its potential use in ADVANCES IN KNOWLEDGE: Medulloblastoma can be classified into four distinct molecular subgroups using radiomic feature classifier from non-invasive Multiparametric Magnetic resonance imaging. This can have future ramifications in the extent of surgical resection of Medulloblastoma which can ultimately result in reduction of morbidity.
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