Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 28, 2026

Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma
09:17

Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma

Published on: September 13, 2022

High-grade glioma diffusive modeling using statistical tissue information and diffusion tensors extracted from

Alexandros Roniotis1, Georgios C Manikis, Vangelis Sakkalis

  • 1Institute of Computer Science, Foundation for Research and Technology, GR-700 13 Heraklion, Greece. roniotis@ics.forth.gr

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society
|October 13, 2011
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Domain Generalization Mitigates Scanner-Induced Domain Shift in Medical Imaging.

Journal of imaging informatics in medicine·2026
Same author

Artificial intelligence-driven radiomics: developing valuable radiomics signatures with the use of artificial intelligence.

BJR artificial intelligence·2026
Same author

Radiomics Applicability Domain Analysis Classification Framework (RADAN-CF): A method for evaluating prediction reliability in radiomics.

Computer methods and programs in biomedicine·2026
Same author

Respiration Rate Estimation Using ECG and PPG Data: Open Source Implementations Focused on Wearables.

IEEE journal of biomedical and health informatics·2026
Same author

Reporting checklist for foundation and large language models in medical research (REFINE): an international consensus guideline.

Diagnostic and interventional radiology (Ankara, Turkey)·2026
Same author

Artificial Intelligence Methods and Digital Intervention Strategies for Predicting and Managing Chronic Obstructive Pulmonary Disease Exacerbations: An Umbrella Review.

Healthcare (Basel, Switzerland)·2025

This study introduces a novel glioma growth model that leverages brain atlases for more accurate simulations. The model improves glioma prognostication by considering white and gray matter proportions and diffusion tensors without requiring diffusion tensor imaging (DTI) processing.

Area of Science:

  • Neuro-oncology
  • Computational biology
  • Medical imaging analysis

Background:

  • Glioma, particularly glioblastoma, presents a significant challenge due to its invasive nature and high fatality rate.
  • Current diffusive models approximate glioma cell invasion using diffusion-reaction equations, with advanced models incorporating heterogeneous white and gray matter velocities and anisotropic migration along white fibers via diffusion tensor imaging (DTI).

Purpose of the Study:

  • To develop and validate a novel glioma growth model that enhances prognostic accuracy.
  • To integrate anatomical information from brain atlases directly into glioma growth simulations, bypassing the need for DTI processing.

Main Methods:

  • The study utilizes proportions of white and gray matter, along with diffusion tensors, extracted directly from normal brain atlases.

More Related Videos

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Related Experiment Videos

Last Updated: May 28, 2026

Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma
09:17

Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma

Published on: September 13, 2022

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

  • This approach avoids the need for DTI processing by directly incorporating anatomical data into the diffusion-reaction framework.
  • The novel model was applied to real patient data for validation.

    • Main Results:

    • The novel glioma growth model successfully integrated anatomical data from brain atlases.
    • Application of the model to real data demonstrated potential for improved glioma prognostication.
    • The method eliminates the requirement for diffusion tensor imaging (DTI) data processing.

    Conclusions:

    • The developed glioma growth model offers a more refined simulation of tumor invasion by directly using anatomical atlas data.
    • This approach simplifies the modeling process by removing the need for DTI processing.
    • The findings suggest a significant improvement in glioma prognostication rates using this atlas-driven method.