Radiomics-Based Machine Learning Classification for Glioma Grading Using Diffusion- and Perfusion-Weighted Magnetic Resonance Imaging.

Journal: Journal of computer assisted tomography

Published Date: May 13, 2025

Abstract

OBJECTIVE: The aim of this study was to evaluate various radiomics-based machine learning classification models using the apparent diffusion coefficient (ADC) and cerebral blood flow (CBF) maps for differentiating between low-grade gliomas (LGGs) and high-grade gliomas (HGGs).

Authors

Takashi Hashido
Shigeyoshi Saito

Department of Medical Physics and Engineering, Division of Health Sciences, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
Takayuki Ishida

Division of Health Sciences, Graduate School of Medicine, Osaka University, 1-7, Yamadaoka, Suita, 565-0871, Japan.

Keywords

Adult Brain Brain Neoplasms Diffusion Magnetic Resonance Imaging Female Glioma Humans Image Interpretation, Computer-Assisted Machine Learning Magnetic Resonance Angiography Male Middle Aged Neoplasm Grading Retrospective Studies

External Resources

View on PubMed Access via DOI PubMed (34270479)

Radiomics-Based Machine Learning Classification for Glioma Grading Using Diffusion- and Perfusion-Weighted Magnetic Resonance Imaging.

Abstract

Authors

Keywords

External Resources

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