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 IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

57
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,...
57

You might also read

Related Articles

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

Sort by
Same author

Current trends in glioma tumor segmentation: A survey of deep learning modules.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)·2025
Same author

Advanced MRI Methods for Diagnosis and Monitoring of Multiple Sclerosis (MS).

Journal of magnetic resonance imaging : JMRI·2025
Same author

Computer-Aided Detection (CADe) and Segmentation Methods for Breast Cancer Using Magnetic Resonance Imaging (MRI).

Journal of magnetic resonance imaging : JMRI·2025
Same author

6-gingerol effect on rat liver following exposure to gold nanoparticles: From histopathologic findings to inflammatory and oxidative stress biomarkers.

Journal of biochemical and molecular toxicology·2024
Same author

Deep Learning-Based Techniques in Glioma Brain Tumor Segmentation Using Multi-Parametric MRI: A Review on Clinical Applications and Future Outlooks.

Journal of magnetic resonance imaging : JMRI·2024
Same author

Status of antibiotic residues in milk and dairy products of Iran: a systematic review and meta-analysis.

Journal of environmental health science & engineering·2024

Related Experiment Video

Updated: Sep 17, 2025

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

382

Enhancing Prostate Cancer Classification: A Comprehensive Review of Multiparametric MRI and Deep Learning

Gelareh Valizadeh1,2, Mahmoud Morafegh1,3, Fatemeh Fatemi2

  • 1Quantitative MR Imaging and Spectroscopy Group (QMISG), Tehran University of Medical Sciences, Tehran, Iran.

Journal of Magnetic Resonance Imaging : JMRI
|July 4, 2025
PubMed
Summary

Artificial intelligence (AI) using deep learning (DL) models shows promise for analyzing multiparametric MRI (mpMRI) in prostate cancer (PCa) detection. These AI tools aim to improve diagnostic accuracy and streamline the interpretation of mpMRI scans for better patient outcomes.

Keywords:
artificial intelligence (AI)computer‐aided diagnosis (CAD)deep learningmultiparametric magnetic resonance imaging (mpMRI)prostate cancer (PCa)prostate cancer classification

More Related Videos

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy
09:11

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy

Published on: April 9, 2019

21.7K
Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:05

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

674

Related Experiment Videos

Last Updated: Sep 17, 2025

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

382
Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy
09:11

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy

Published on: April 9, 2019

21.7K
Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:05

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

674

Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Medicine
  • Oncology

Background:

  • Multiparametric MRI (mpMRI) is crucial for prostate cancer (PCa) detection, potentially reducing unnecessary biopsies.
  • Interpreting prostate mpMRI is subjective and complex, posing challenges with increasing adoption.
  • Artificial intelligence (AI), particularly deep learning (DL), offers a solution to enhance mpMRI analysis.

Purpose of the Study:

  • To review the integration of DL classification networks with mpMRI for PCa assessment.
  • To examine DL network architectures, MRI sequence impacts, and the value of incorporating clinical data.
  • To discuss AI-assisted predictions, model explainability, and clinical integration of DL systems.

Main Methods:

  • Review of DL classification networks applied to prostate mpMRI data.
  • Analysis of various network architectures and input MRI sequences.
  • Evaluation of strategies for incorporating domain knowledge and clinical information.

Main Results:

  • DL models can differentiate benign tissue from PCa, classify disease significance, and predict high-grade cancer.
  • Performance of DL models is influenced by MRI sequence inputs and data quality.
  • Comparisons with PI-RADS show potential for AI in PCa diagnosis.

Conclusions:

  • DL-based AI holds significant potential to improve the accuracy and efficiency of prostate cancer detection using mpMRI.
  • Enhancing model explainability and interpretability is key for clinical trust and adoption.
  • AI-assisted systems can optimize the prostate MRI reporting workflow, addressing current limitations for future integration.