Because of variations in severity and treatment methods of pilocytic astrocytoma, medulloblastoma, and ependymoma, accurate and specific diagnoses of the tumors are critical. Non-invasive diagnosis of posterior fossa tumors based on machine learning-based magnetic resonance imaging are being reported. However, conventional MRI, diffusion MRI, MR perfusion, and magnetic resonance spectroscopy have variable diagnostic values. We present here a meta-analysis of all the relevant published studies and conducted a large sample-size assessment concerning the diagnostic performance and potential covariates that could influence the diagnostic performance of machine learning.
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