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 Concept Videos

Imaging Studies for Cardiovascular System IV: CMRI01:21

Imaging Studies for Cardiovascular System IV: CMRI

293
Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...
293
Imaging Studies VII: Vascular Imaging01:19

Imaging Studies VII: Vascular Imaging

284
DefinitionRenal angiography, also known as renal arteriography, is an imaging technique used to obtain a comprehensive view of blood flow and the vascular structure of blood vessels in the kidneys and surrounding areas.PurposeRenal angiography detects blood vessel abnormalities in the kidneys, such as aneurysms, stenosis, thrombosis, vascular tumors, and renal artery stenosis. It evaluates kidney function and guides interventional treatments like angioplasty or stent placement.Pre-Procedure...
284

You might also read

Related Articles

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

Sort by
Same author

A γ-radiation-responsive diselenide-core micelle for on-demand delivery of cannabidiol in the mitigation of acute radiation injury.

Materials today. Bio·2026
Same author

Trajectories of grip strength decline and risk of new-onset cardiovascular disease: evidence from the HRS and ELSA cohorts.

Frontiers in public health·2026
Same author

Refined AI-ASPECTS with modified atlas and lesion-load thresholds: advancing acute ischemic stroke imaging and prognostic prediction.

BMC medicine·2026
Same author

MicroRNA expression profiling and functional analysis of CDH3 during oogenesis in the Chinese alligator (<i>Alligator sinensis</i>).

Current zoology·2026
Same author

Exploring resting-state network dysconnectivity in schizophrenia with single-subject ICA.

CNS spectrums·2026
Same author

Resting-state EEG alpha-BOLD coupling spatially follows cortical cell-type and receptor gradients.

bioRxiv : the preprint server for biology·2026

Related Experiment Video

Updated: Jan 7, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

49.2K

Spatial radiomics-based interpretable multimodal machine learning model enhances outcomes prediction for minor

Liang Jiang1, Yi Zhou2, Qiang Xu2

  • 1Departments of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Postdoctoral Research Station, Nanjing Medical University, Nanjing, China.

Journal of Advanced Research
|December 30, 2025
PubMed
Summary

A new spatial radiomics model accurately predicts unfavorable outcomes in minor stroke patients by analyzing lesion connectivity. This interpretable machine learning approach improves upon conventional methods for better clinical management.

Keywords:
Diffusion weighted imagingMachine learningMinor strokeOutcomesRadiomics

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

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

Published on: October 10, 2018

8.6K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.1K

Related Experiment Videos

Last Updated: Jan 7, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

49.2K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

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

Published on: October 10, 2018

8.6K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.1K

Area of Science:

  • Neuroimaging
  • Machine Learning
  • Stroke Medicine

Background:

  • Accurate prediction of unfavorable outcomes is critical for managing minor stroke.
  • Conventional radiomics models often overlook spatial lesion properties crucial for clinical insights.

Purpose of the Study:

  • To develop and validate a novel spatial radiomics-interpretable model using machine learning to predict minor stroke outcomes.
  • To quantitatively extract spatial features of lesions at various topological levels.

Main Methods:

  • A cohort of 4,164 minor stroke patients was analyzed.
  • Voxel-based and normative connection lesion analyses quantified spatial infarct features.
  • A hybrid spatial radiomics model integrated these features with machine learning classifiers, interpreted using SHAP.

Main Results:

  • The spatial radiomics model achieved superior prediction accuracy (AUC: 0.95/0.88/0.87) compared to conventional radiomics.
  • Key predictors of unfavorable outcomes included lesion disconnection in corticospinal tracts, spinocerebellar tracts, and default mode regions.

Conclusions:

  • The spatial radiomics model significantly enhances the prediction of unfavorable outcomes in minor stroke.
  • This approach offers a novel, interpretable method within spatial-omics for improved stroke management.