AIMC Topic: Cerebellar Neoplasms

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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...
Humans Machine Learning Decision Support Techniques Prognosis Clinical Decision-Making Female Reproducibility of Results Risk Factors Young Adult Adult Biomarkers, Tumor Male Middle Aged Risk Assessment Infant Gene Expression Profiling Transcriptome Adolescent Child, Preschool Child Nomograms Databases, Genetic Predictive Value of Tests Cerebellar Neoplasms Medulloblastoma
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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 ...
Humans Algorithms Child Support Vector Machine Principal Component Analysis Cerebellar Neoplasms Medulloblastoma
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Deep learning-based automatic tumor burden assessment of pediatric high-grade gliomas, medulloblastomas, and other leptomeningeal seeding tumors.

Neuro-oncology Feb 1, 2022
BACKGROUND: Longitudinal measurement of tumor burden with magnetic resonance imaging (MRI) is an essential component of response assessment in pediatric brain tumors. We developed a fully automated pipeline for the segmentation of tumors in pediatric...
Humans Prospective Studies Deep Learning Child Image Processing, Computer-Assisted Glioma Magnetic Resonance Imaging Tumor Burden Cerebellar Neoplasms Medulloblastoma
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