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

Radiological Investigation II: MRI and Ventilation Perfusion Scan01:30

Radiological Investigation II: MRI and Ventilation Perfusion Scan

452
Description
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
MRI
MRI uses magnetic fields and radiofrequency signals to distinguish between normal and abnormal tissues. This technology provides a more detailed diagnostic image than CT scans, enabling it to characterize pulmonary nodules, stage bronchogenic carcinoma, and evaluate inflammatory activity in...
452

You might also read

Related Articles

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

Sort by
Same author

Association between intra- and peritumoral heterogeneity on B-mode ultrasound and overall survival in patients with liver metastases treated with immunotherapy.

European radiology experimental·2026
Same author

Estimating Metastatic Disease Heterogeneity Using Radiomics-based Lesion Clustering and Heterogeneity Index - Application in Lung Adenocarcinoma Patients and Correlations With Patient Outcomes.

Journal of imaging informatics in medicine·2026
Same author

Multimodal imaging to analyze the biomechanical properties of kidney tumors, evaluating feasibility, inter-modality correspondence, and diagnostic value (UroCCR-115).

PloS one·2026
Same author

Risk factors associated with significant posttraumatic brain hemorrhage as per the QueBIC categories: A retrospective multicenter study.

The journal of trauma and acute care surgery·2026
Same author

Deep Learning Predicts Mutations and Outcomes in Gastrointestinal Stromal Tumors from Whole-Slide Images.

Cancer research·2026
Same author

Predictive Factors of Survival in Patients with Iliopsoas and Anterior Abdominal Wall Hematomas: A 10-Year Retrospective Study Focused on Transarterial Embolization.

Journal of vascular and interventional radiology : JVIR·2026

Related Experiment Video

Updated: Dec 31, 2025

Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model
06:24

Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model

Published on: April 18, 2015

15.5K

High-Grade Soft-Tissue Sarcomas: Can Optimizing Dynamic Contrast-Enhanced MRI Postprocessing Improve Prognostic

Amandine Crombé1,2,3, David Fadli1, Xavier Buy1

  • 1Department of Radiology, Institut Bergonie, Bordeaux, France.

Journal of Magnetic Resonance Imaging : JMRI
|January 11, 2020
PubMed
Summary

Pretreatment dynamic contrast-enhanced MRI (DCE-MRI) in soft tissue sarcomas (STS) offers prognostic insights. Relative changes in radiomics features (rRFs) from DCE-MRI are more predictive of outcomes than parametric features.

Keywords:
DCE-MRImachine-learningradiomicsresponse evaluationsoft-tissue sarcomasurvival analysis

More Related Videos

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform
07:57

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

3.1K
Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

13.6K

Related Experiment Videos

Last Updated: Dec 31, 2025

Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model
06:24

Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model

Published on: April 18, 2015

15.5K
Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform
07:57

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

3.1K
Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

13.6K

Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging

Background:

  • Pretreatment dynamic contrast-enhanced MRI (DCE-MRI) heterogeneity in sarcomas may indicate prognosis.
  • The optimal method for extracting prognostic data from baseline DCE-MRI remains undetermined.

Purpose of the Study:

  • To identify the most effective method for extracting prognostic information from baseline DCE-MRI in sarcomas.

Main Methods:

  • Radiomics features (RFs) and their relative changes (rRFs) were extracted from DCE-MRI at multiple time points.
  • Integrated rRFs (irRFs) and parametric RFs (pRFs) were computed.
  • Machine learning models were developed using RFs, rRFs, irRFs, pRFs, conventional radiological features, and T2-weighted imaging (T2-WI) RFs.

Main Results:

  • Models based on rRFs and irRFs showed significant correlation with prognosis.
  • Models incorporating all rRFs and irRFs achieved the highest concordance index (0.83).
  • The conventional radiological model demonstrated the highest area under the curve (0.87), closely followed by the all rRFs and irRFs models.

Conclusions:

  • Baseline DCE-MRI of soft tissue sarcomas (STS) contains valuable prognostic information.
  • Predictive modeling using relative changes in radiomics features (rRFs) appears more effective than using parametric features (pRFs).