Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

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

A relational process of newcomer identity development: The role of proactive relationship-building behavior through respect.

Acta psychologica·2026

Same author

Machine learning-enhanced non-amnestic Alzheimer's disease diagnosis from MRI and clinical features.

ArXiv·2026

Same author

Functional brain network identification in opioid use disorder using machine learning analysis of resting-state fMRI BOLD signals.

Computers in biology and medicine·2025

Same author

AI generated annotations for Breast, Brain, Liver, Lungs, and Prostate cancer collections in the National Cancer Institute Imaging Data Commons.

Scientific data·2025

Same author

Functional Brain Network Identification in Opioid Use Disorder Using Machine Learning Analysis of Resting-State fMRI BOLD Signals.

ArXiv·2024

Same author

AI-Generated Annotations Dataset for Diverse Cancer Radiology Collections in NCI Image Data Commons.

Scientific data·2024

Same journal

Relationship between spontaneous EEG oscillations at 7 and 45 days of acute plateau exposure and the plateau acclimatization index.

Frontiers in neuroscience·2026

Same journal

Neuroprotective effects of paederoside against mitochondrial dysfunction in rotenone-induced cell models of Parkinson's disease.

Frontiers in neuroscience·2026

Same journal

Covariance-based analysis of spindle-band EEG during declarative and non-declarative odor cueing in sleep.

Frontiers in neuroscience·2026

Same journal

Correction: Physiological determinants of cortical P100 responses in pattern visual evoked potentials: a scoping review.

Frontiers in neuroscience·2026

Same journal

Transcranial magnetic stimulation and motor overflow: a systematic review in neurological disorders.

Frontiers in neuroscience·2026

Same journal

Editorial: Advancing neurodegenerative disease biomarkers: the role of neuroimaging in TDP-43 and tau proteinopathies.

Frontiers in neuroscience·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 5, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Feature-Guided Deep Radiomics for Glioblastoma Patient Survival Prediction.

Zeina A Shboul¹, Mahbubul Alam¹, Lasitha Vidyaratne¹

¹Vision Lab in Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA, United States.

Frontiers in Neuroscience

|October 18, 2019

Summary

This summary is machine-generated.

This study introduces an automated framework for segmenting glioblastoma (WHO grade IV glioma) and predicting patient survival using MRI scans. The AI-driven approach improves accuracy and reduces variability compared to manual methods.

Keywords:

glioblastoma neural network radiomics segmentation survival prediction

More Related Videos

Author Spotlight: Multimodal Imaging Strategies for Optimizing Drug Delivery and Early Detection in Glioblastoma Treatment

Author Spotlight: Multimodal Imaging Strategies for Optimizing Drug Delivery and Early Detection in Glioblastoma Treatment

Published on: March 1, 2024

Translational Orthotopic Models of Glioblastoma Multiforme

Translational Orthotopic Models of Glioblastoma Multiforme

Published on: February 17, 2023

Related Experiment Videos

Last Updated: Jan 5, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Author Spotlight: Multimodal Imaging Strategies for Optimizing Drug Delivery and Early Detection in Glioblastoma Treatment

Author Spotlight: Multimodal Imaging Strategies for Optimizing Drug Delivery and Early Detection in Glioblastoma Treatment

Published on: March 1, 2024

Translational Orthotopic Models of Glioblastoma Multiforme

Translational Orthotopic Models of Glioblastoma Multiforme

Published on: February 17, 2023

Area of Science:

Neuro-oncology
Medical Imaging Analysis
Artificial Intelligence in Medicine

Background:

Glioblastoma (WHO grade IV glioma) is an aggressive brain tumor requiring accurate diagnosis and prognosis.
Current MRI-based diagnosis involves manual tumor segmentation, which is time-consuming and prone to inter-observer variability.
Precise segmentation of tumor tissues is crucial for accurate survival prediction in glioblastoma patients.

Purpose of the Study:

To develop and evaluate a fully automated framework for glioblastoma segmentation and survival prediction using MRI.
To enhance diagnostic accuracy and prognostic capabilities by reducing manual intervention and variability.
To investigate the efficacy of radiomics features in automated segmentation and survival prediction.

Main Methods:

A deep neural network framework guided by radiomics features for automated segmentation of glioblastoma and abnormal tissues in MRI.
Integration of segmented tumor tissues and clinical features into two distinct survival prediction pipelines (regression and classification).
Validation of the framework using benchmark datasets from the Brain Tumor Segmentation (BraTS) challenges (2017, 2018).

Main Results:

The automated framework achieved high performance in segmenting glioblastoma and predicting patient survival.
The best survival prediction pipeline demonstrated leave-one-out cross-validation accuracies of 0.73 (training) and 0.68 (validation).
These accuracies represent some of the highest reported in the literature for glioblastoma survival prediction.

Conclusions:

The proposed automated framework significantly improves glioblastoma segmentation and survival prediction accuracy.
The AI-driven approach offers a more efficient and reliable alternative to manual methods, reducing inter-observer variability.
Radiomics features play a vital role in enhancing both segmentation and survival prediction performance, offering valuable insights.