AIMC Topic: Medulloblastoma
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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
Jun 30, 2025
BACKGROUND: Medulloblastoma (MB) is the most common malignant brain tumor in children. Current diagnostic methods, such as MRI and lumbar puncture, are invasive and not sensitive enough, making early diagnosis challenging. MicroRNAs (miRNAs) have eme...
Machine Learning-Driven Identification of Molecular Subgroups in Medulloblastoma via Gene Expression Profiling.
Clinical oncology (Royal College of Radiologists (Great Britain))
Feb 11, 2025
AIMS: Medulloblastoma (MB) is the most prevalent malignant brain tumour in children, characterised by substantial molecular heterogeneity across its subgroups. Accurate classification is pivotal for personalised treatment strategies and prognostic as...
Deep learning to decode sites of RNA translation in normal and cancerous tissues.
Nature communications
Feb 2, 2025
The biological process of RNA translation is fundamental to cellular life and has wide-ranging implications for human disease. Accurate delineation of RNA translation variation represents a significant challenge due to the complexity of the process a...
Machine learning-based models for prediction of survival in medulloblastoma: a systematic review and meta-analysis.
Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Nov 12, 2024
BACKGROUND: Medulloblastoma (MB) is the pediatric population's most frequent malignant intracranial lesions. Prognostication plays a crucial role in optimizing treatment strategy in the MB setting. Several studies have developed ML-based models to pr...
Deep learning models for predicting the survival of patients with medulloblastoma based on a surveillance, epidemiology, and end results analysis.
Scientific reports
Jun 24, 2024
Medulloblastoma is a malignant neuroepithelial tumor of the central nervous system. Accurate prediction of prognosis is essential for therapeutic decisions in medulloblastoma patients. We analyzed data from 2,322 medulloblastoma patients using the SE...
Multiparametric MRI-Based Interpretable Radiomics Machine Learning Model Differentiates Medulloblastoma and Ependymoma in Children: A Two-Center Study.
Academic radiology
Mar 20, 2024
RATIONALE AND OBJECTIVES: Medulloblastoma (MB) and Ependymoma (EM) in children, share similarities in age group, tumor location, and clinical presentation. Distinguishing between them through clinical diagnosis is challenging. This study aims to expl...
Automatic image segmentation and online survival prediction model of medulloblastoma based on machine learning.
European radiology
Nov 23, 2023
OBJECTIVES: To develop a dynamic nomogram containing radiomics signature and clinical features for estimating the overall survival (OS) of patients with medulloblastoma (MB) and design an automatic image segmentation model to reduce labor and time co...
Classification of paediatric brain tumours by diffusion weighted imaging and machine learning.
Scientific reports
Feb 4, 2021
To determine if apparent diffusion coefficients (ADC) can discriminate between posterior fossa brain tumours on a multicentre basis. A total of 124 paediatric patients with posterior fossa tumours (including 55 Medulloblastomas, 36 Pilocytic Astrocyt...
Two machine learning methods identify a metastasis-related prognostic model that predicts overall survival in medulloblastoma patients.
Aging
Nov 5, 2020
Approximately 30% of medulloblastoma (MB) patients exhibit metastasis at initial diagnosis, which often leads to a poor prognosis. Here, by using univariate Cox regression analysis, two machine learning methods (Lasso-penalized Cox regression and ran...
Risk Assessment
Machine Learning
Infant
Decision Support Techniques
Gene Expression Profiling
Prognosis
Transcriptome
Clinical Decision-Making
Adult
Female
Biomarkers, Tumor
Databases, Genetic
Reproducibility of Results
Male
Cerebellar Neoplasms
Predictive Value of Tests
Risk Factors
Adolescent
Humans
Middle Aged
Medulloblastoma
Young Adult
Child, Preschool
Child
Nomograms
Study on Contribution of Biological Interpretable and Computer-Aided Features Towards the Classification of Childhood Medulloblastoma Cells.
Journal of medical systems
Jul 4, 2018
Diagnosis and Prognosis of brain tumour in children is always a critical case. Medulloblastoma is that subtype of brain tumour which occurs most frequently amongst children. Post-operation, the classification of its subtype is most vital for further ...
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