Assessing Versatile Machine Learning Models for Glioma Radiogenomic Studies across Hospitals

Assessing Versatile Machine Learning Models for Glioma Radiogenomic Studies across Hospitals

Radiogenomics use non-invasively obtained imaging data, such as magnetic resonance imaging (MRI), to predict critical biomarkers of patients. Developing an accurate machine learning (ML) technique for MRI requires data from hundreds of patients, which cannot be gathered from any single local hospita...

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Bibliographic Details
Main Authors: Risa K. Kawaguchi, Masamichi Takahashi, Mototaka Miyake, Manabu Kinoshita, Satoshi Takahashi, Koichi Ichimura, Ryuji Hamamoto, Yoshitaka Narita, Jun Sese
Format: Article
Language:English
Published: MDPI AG 2021-07-01
Series:Cancers
Subjects:
glioma
machine learning
radiogenomics
IDH
MGMT
Online Access:https://www.mdpi.com/2072-6694/13/14/3611

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https://www.mdpi.com/2072-6694/13/14/3611

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