Editorial for “Differentiation Between High‐Grade Glioma and Brain Metastasis Using Cerebral Perfusion‐Related Parameters (Cerebral Blood Volume and Cerebral Blood Flow): A Systematic Review and Meta‐Analysis of Perfusion‐weighted MRI Techniques” DOI
Wei Chen, Shiman Wu

Journal of Magnetic Resonance Imaging, Journal Year: 2024, Volume and Issue: unknown

Published: July 6, 2024

Language: Английский

A Systematic Review of Diffusion Microstructure Imaging (DMI): Current and Future Applications in Neurology Research DOI Creative Commons
Sadegh Ghaderi, Sana Mohammadi, Farzad Fatehi

et al.

Brain Disorders, Journal Year: 2025, Volume and Issue: unknown, P. 100238 - 100238

Published: May 1, 2025

Language: Английский

Citations

0

Quantitative Physiologic MRI Combined with Feature Engineering for Developing Machine Learning-Based Prediction Models to Distinguish Glioblastomas from Single Brain Metastases DOI Creative Commons
Seyyed Ali Hosseini, Stijn Servaes,

Brandon Hall

et al.

Diagnostics, Journal Year: 2024, Volume and Issue: 15(1), P. 38 - 38

Published: Dec. 27, 2024

Background: The accurate and early distinction of glioblastomas (GBMs) from single brain metastases (BMs) provides a window opportunity for reframing treatment strategies enabling optimal timely therapeutic interventions. We sought to leverage physiologically sensitive parameters derived diffusion tensor imaging (DTI) dynamic susceptibility contrast (DSC)–perfusion-weighted (PWI) along with machine learning-based methods distinguish GBMs BMs. Methods: Patients histopathology-confirmed (n = 62) BMs 26) exhibiting contrast-enhancing regions (CERs) underwent 3T anatomical imaging, DTI DSC-PWI prior treatment. Median values mean diffusivity (MD), fractional anisotropy, linear, planar spheric anisotropic coefficients, relative cerebral blood volume (rCBV) maximum rCBV were measured CERs immediate peritumor regions. Data normalization scaling performed. In the next step, most relevant features extracted (non-interacting features), which subsequently used generate set new, innovative, high-order (interacting features) using feature engineering method. Finally, 10 learning classifiers employed in distinguishing Cross-validation receiver operating characteristic (ROC) curve analyses performed determine diagnostic performance. Results: A random forest classifier ANOVA F-value selection algorithm both interacting non-interacting provided best performance an area under ROC 92.67%, classification accuracy 87.8%, sensitivity 73.64% specificity 97.5%. Conclusions: based approach involving combined use physiological MRI shows promise differentiate between high accuracy.

Language: Английский

Citations

0

Editorial for “Differentiation Between High‐Grade Glioma and Brain Metastasis Using Cerebral Perfusion‐Related Parameters (Cerebral Blood Volume and Cerebral Blood Flow): A Systematic Review and Meta‐Analysis of Perfusion‐weighted MRI Techniques” DOI
Wei Chen, Shiman Wu

Journal of Magnetic Resonance Imaging, Journal Year: 2024, Volume and Issue: unknown

Published: July 6, 2024

Language: Английский

Citations

0