AIMC Topic: Infratentorial Neoplasms

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Deep Learning for Pediatric Posterior Fossa Tumor Detection and Classification: A Multi-Institutional Study.

AJNR. American journal of neuroradiology Aug 13, 2020
BACKGROUND AND PURPOSE: Posterior fossa tumors are the most common pediatric brain tumors. MR imaging is key to tumor detection, diagnosis, and therapy guidance. We sought to develop an MR imaging-based deep learning model for posterior fossa tumor d...
Humans Female Young Adult Adult Male Infant Deep Learning Adolescent Child, Preschool Child Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Infratentorial Neoplasms
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Ependymoma and pilocytic astrocytoma: Differentiation using radiomics approach based on machine learning.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia Apr 23, 2020
Mandatory accurate and specific diagnosis demands have brought about increased challenges for radiologists in pediatric posterior fossa tumor prediction and prognosis. With the development of high-performance computing and machine learning technologi...
Humans Machine Learning Female Infant, Newborn Male Infant Adolescent Child, Preschool Child Magnetic Resonance Imaging Diagnosis, Differential Image Interpretation, Computer-Assisted Astrocytoma Ependymoma Infratentorial Neoplasms
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Automatic Machine Learning to Differentiate Pediatric Posterior Fossa Tumors on Routine MR Imaging.

AJNR. American journal of neuroradiology Jul 1, 2020
BACKGROUND AND PURPOSE: Differentiating the types of pediatric posterior fossa tumors on routine imaging may help in preoperative evaluation and guide surgical resection planning. However, qualitative radiologic MR imaging review has limited performa...
Humans Machine Learning Retrospective Studies Female Male Infant Adolescent Child, Preschool Child Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Neuroimaging Infratentorial Neoplasms
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