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

5.1K
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...
5.1K

You might also read

Related Articles

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

Sort by
Same author

Predicting Recurrence Risk of Glioblastoma Based on Preoperative-Postoperative Longitudinal MRI: A Multicenter Study.

Bioengineering (Basel, Switzerland)·2026
Same author

Cholesterol-Mediated Metabolic-mechanotransductive Crosstalk Orchestrates Castration Resistance in Prostate Cancer.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

CD248 acts as a mechanosensory switch in fibroblast subsets to establish distinct pathological niches in renal fibrosis.

Nature communications·2026
Same author

PSMA-Targeting Macrophage Membrane-Coated Nanoparticles for Precision Diagnosis and Combination Therapy of Prostate Cancer.

Exploration (Beijing, China)·2026
Same author

Simulation-Based X-Ray Spectrum Optimization for Dose Enhancement in X-Ray-Induced Photodynamic Therapy with NaLuF<sub>4</sub>:20% Gd, 15% Tb<sup>3+</sup> Nanocrystals.

Bioengineering (Basel, Switzerland)·2026
Same author

Integrative Cross-Modal Fusion of Preoperative MRI and Histopathological Signatures for Improved Survival Prediction in Glioblastoma.

Bioengineering (Basel, Switzerland)·2026

Related Experiment Video

Updated: Jun 29, 2025

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

21.5K

Detecting MRI-Invisible Prostate Cancers Using a Weakly Supervised Deep Learning Model.

Yao Zheng1, Jingliang Zhang2, Dong Huang1

  • 1School of Biomedical Engineering, Air Force Medical University, No. 169 Changle West Road, Xi'an, Shaanxi, China.

International Journal of Biomedical Imaging
|March 27, 2024
PubMed
Summary

A new AI model, weakly supervised UNet (WSUNet), effectively detects MRI-invisible prostate cancers (MIPCas). This reduces unnecessary biopsies by improving precision and decreasing the number of biopsy needles needed.

More Related Videos

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

189
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

Related Experiment Videos

Last Updated: Jun 29, 2025

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

21.5K
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

189
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

Area of Science:

  • Radiology
  • Artificial Intelligence
  • Oncology

Background:

  • Magnetic Resonance Imaging (MRI) is crucial for prostate cancer detection.
  • MRI-invisible prostate cancers (MIPCas) present diagnostic challenges due to similar appearances to normal tissue.
  • Extensive systematic biopsy is often required for MIPCas identification.

Purpose of the Study:

  • To develop and validate a weakly supervised UNet (WSUNet) model for detecting MIPCas.
  • To assess the efficacy of WSUNet in improving prostate cancer diagnosis and reducing unnecessary biopsies.

Main Methods:

  • A cohort of 777 patients (600 training, 177 testing) underwent MRI-ultrasound fusion guided biopsies.
  • MIPCas were identified based on Gleason grade (≥7) from systematic biopsy results.
  • WSUNet model was developed and validated using the testing set.

Main Results:

  • WSUNet achieved an AUC of 0.764 (95% CI: 0.728-0.798) in the testing set.
  • The model demonstrated a 91.3% precision improvement (p < 0.01) over conventional methods.
  • Unnecessary biopsy needles decreased by 47.6% (p < 0.01) while maintaining detection rates.

Conclusions:

  • The developed WSUNet model effectively detects MRI-invisible prostate cancers.
  • WSUNet significantly reduces the need for unnecessary prostate biopsies.