Intervening Nidal Brain Parenchyma and Risk of Radiation-Induced Changes After Radiosurgery for Brain Arteriovenous Malformation: A Study Using an Unsupervised Machine Learning Algorithm.

Lee, Cheng-Chia

Intervening Nidal Brain Parenchyma and Risk of Radiation-Induced Changes After Radiosurgery for Brain Arteriovenous Malformation: A Study Using an Unsupervised Machine Learning Algorithm. [electronic resource] - World neurosurgery 05 2019 - e132-e138 p. digital

Publication Type: Journal Article

1878-8769

10.1016/j.wneu.2018.12.220 doi


Adolescent
Adult
Aged
Algorithms
Brain--radiation effects
Child
Female
Humans
Intracranial Arteriovenous Malformations--radiotherapy
Male
Middle Aged
Parenchymal Tissue--radiation effects
Prospective Studies
Radiation Injuries--etiology
Radiosurgery--adverse effects
Risk Factors
Unsupervised Machine Learning
Young Adult