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

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...

You might also read

Related Articles

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

Sort by
Same author

Integrating problem-based learning with virtual simulation in trauma orthopedic education: effects on learning outcomes and student satisfaction.

BMC medical education·2026
Same author

Fuzzy multi-criteria selection of sensor architectures for coastal pollution monitoring.

Scientific reports·2026
Same author

Review of Magnetic Adsorbents for Heavy Metals in Sludge Leachate: Synthesis, Mechanism, and Performance Evaluation.

Materials (Basel, Switzerland)·2026
Same author

Fourier Analysis of Bilateral Breast Asymmetry for Short-term Breast Cancer Risk Prediction.

medRxiv : the preprint server for health sciences·2026
Same author

Single-cell TCR mapping reveals spatially coordinated T cell states in head and neck cancer.

Science immunology·2026
Same author

Long-term efficacy of the treat-to-close strategy for patients with atrial septal defect-pulmonary artery hypertension and characteristics of indicated populations.

Clinics (Sao Paulo, Brazil)·2026

Related Experiment Video

Updated: May 13, 2026

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

22.4K

Quantitative DCE Dynamics on Transformed MR Imaging Discriminates Clinically Significant Prostate Cancer.

Zhouping Wei1, Malinda Iluppangama1,2, Jin Qi3

  • 1Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA.

Cancer Control : Journal of the Moffitt Cancer Center
|November 15, 2024
PubMed
Summary
This summary is machine-generated.

Quantifying dynamic contrast enhancement (DCE) MRI features using radiomic transformations improves prostate cancer detection. This method enhances signal standardization and temporal analysis for better discrimination of aggressive disease.

Keywords:
DCEMRIhabitatsmachine learningprostate cancerradiomics

More Related Videos

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

15.1K
A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
06:08

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

139

Related Experiment Videos

Last Updated: May 13, 2026

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

22.4K
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

15.1K
A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
06:08

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

139

Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging Analysis

Background:

  • Dynamic contrast enhancement (DCE) is a key multiparametric MRI (mpMRI) sequence for prostate cancer detection.
  • Current DCE assessment is largely qualitative, limiting its diagnostic potential.
  • Quantitative analysis of DCE features can improve early detection and characterization of prostate cancer.

Purpose of the Study:

  • To propose and validate a quantitative methodology for DCE MRI analysis in prostate cancer.
  • To assess the efficacy of radiomic feature transformations on DCE images for disease characterization.
  • To improve the discrimination between indolent and aggressive prostate cancer using quantitative DCE metrics.

Main Methods:

  • Computed six time-dependent metrics on 306 radiomic features from transformed DCE (T1W) MRI sequences.
  • Applied methodology to 25 prostate cancer patients with pathological Gleason Score confirmation.
  • Utilized T2W MRI and whole-mount pathology for guiding region of abnormality assessment.
  • Employed logistic regression with feature-based transformations (e.g., Centre of Mass) and SMOTE for classifier model development.

Main Results:

  • Preliminary analysis revealed significant differences (P ≤ 0.05, q ≤ 0.01) in temporal DCE features between aggressive and indolent disease.
  • Classifier models using DCE features after Centre of Mass transformation achieved an AUC of 0.89-0.94.
  • Models using mean feature transformation yielded AUCs of 0.71-0.76, validated via bootstrap cross-validation and SMOTE.

Conclusions:

  • Radiomic transformation of DCE MRI (T1 sequences) offers improved signal standardization.
  • Temporal characteristics of transformed DCE features enhance the discrimination of aggressive prostate cancer.
  • Quantitative DCE analysis holds promise for improved prostate cancer assessment and management.