ArticleNeuro-oncology2019
Nomograms based on preoperative multiparametric magnetic resonance imaging for prediction of molecular subgrouping in medulloblastoma: results from a radiogenomics study of 111 patients.
Article in Neuro-oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
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Who cites it
40 citing papers in PubMed, 1 synthesis or guideline pooled it, 68 citations in OpenAlex.
- Arterial spin labelling and diffusion-weighted imaging in paediatric brain tumours.NeuroImage. Clinical · 2019Pooled it
- Development and validation of a radiomics model of magnetic resonance for predicting liver metastasis in resectable pancreatic ductal adenocarcinoma patients.Radiation oncology (London, England) · 2023Trial
- AOSNP-ADAPTR resource level-based recommendations on practical diagnostic strategies for medulloblastomas and other embryonal tumors.Brain pathology (Zurich, Switzerland) · 2026Article
- Dual-task deep learning model for prediction of medulloblastoma molecular subgroups with preoperative brain MRI.La Radiologia medica · 2026Article
- Radiomics in pediatric brain tumors: from images to insights.Discover oncology · 2025Review
- Multiparametric MRI-based machine learning system of molecular subgroups and prognosis in medulloblastoma.European radiology · 2025Article
- The predictive value of pre-treatment MRI-based radiomics and clinical characteristics for medulloblastoma recurrence in pediatric patients.Frontiers in neurology · 2025Article
- Deep Learning of radiology-genomics integration for computational oncology: A mini review.Computational and structural biotechnology journal · 2024Review
- Deep learning models for predicting the survival of patients with medulloblastoma based on a surveillance, epidemiology, and end results analysis.Scientific reports · 2024Article
- Article
- Development and external validation of a novel score for predicting postoperative 30‑day mortality in tumor craniotomy patients: A cross‑sectional diagnostic study.Oncology letters · 2024Article
- Pediatric WNT medulloblastoma predisposition in intraoperative blood loss: a retrospective observational cohort study.Frontiers in neurology · 2024Article
- Neurosurgical Management of Cerebrospinal Tumors in the Era of Artificial Intelligence : A Scoping Review.Journal of Korean Neurosurgical Society · 2023Article
- The Site of Origin of Medulloblastoma: Surgical Observations Correlated to Molecular Groups.Cancers · 2023Article
- Radiogenomics: a key component of precision cancer medicine.British journal of cancer · 2023Review
- Opportunities and Advances in Radiomics and Radiogenomics for Pediatric Medulloblastoma Tumors.Diagnostics (Basel, Switzerland) · 2023Review
- Clinical, Histological, and Molecular Prognostic Factors in Childhood Medulloblastoma: Where Do We Stand?Diagnostics (Basel, Switzerland) · 2023Review
- Survival nomogram for medulloblastoma and multi-center external validation cohort.Frontiers in pharmacology · 2023Article
- The role of artificial intelligence in paediatric neuroradiology.Pediatric radiology · 2022Review
- Machine-learning approach to predict molecular subgroups of medulloblastoma using multiparametric MRI-based tumor radiomics.The British journal of radiology · 2022Article
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Authors and funding
13 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Novel biological insights have led to consensus classification of medulloblastoma into 4 distinct molecular subgroups-wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4. We aimed to predict molecular subgrouping in medulloblastoma based on preoperative multiparametric magnetic resonance imaging (MRI) characteristics. Methods: A set of 19 MRI features were evaluated in 111 patients with histologic diagnosis of medulloblastoma for prediction of molecular subgrouping. MRI characteristics were correlated with molecular subgroups derived from tissue samples in 111 patients (WNT = 17, SHH = 44, Group 3 = 27, and Group 4 = 23). Multinomial logistic regression of imaging parameters was performed on a training cohort (TC) of 76 patients, representing two-thirds of randomly selected patients from each of 4 molecular subgroups, to generate binary nomograms. Validation of these nomograms was performed on the remaining 35 patients as the validation cohort (VC). Results: Medulloblastoma subgroups could be accurately predicted by preoperative MRI features in 74% of cases. Predictive accuracy was excellent for SHH (95%), acceptably high for Group 4 (78%), modest for Group 3 (56%) and worst for WNT (41%) subgroup medulloblastoma. SHH-specific nomogram was associated with excellent correlation between TC and VC, with area under the curve (AUC) of 0.939 and 0.991, respectively. AUC for Group 4 was acceptable at 0.851 and 0.788 in TC and VC, respectively; however, these values were consistently suboptimal in WNT and Group 3 medulloblastoma. Conclusion: The predictive accuracy of MRI-based nomograms was excellent for SHH and encouraging for Group 4 medulloblastoma. Further work is needed for Group 3 and WNT-pathway medulloblastoma.
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