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Abstract #0947

Diagnostic performance of machine learning-based MRI for posterior fossa tumors: a meta-analysis

Chen Chen1, Fabao Gao1, and Xiaoyue Zhou2
1Department of Radiology, West China Hospital, Chengdu, China, 2MR Collaboration, Siemens Healthineers Ltd., Shanghai, China

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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