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

You might also read

Related Articles

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

Sort by
Same author

Request and reporting models for computed tomography in the multidisciplinary management of cancer patients: consensus between the Italian Society of Medical and Interventional Radiology (SIRM) and the Italian Society of Medical Oncology (AIOM).

La Radiologia medica·2026
Same author

Artificial Intelligence-Assisted Quantification of Longitudinal HRCT Changes During Treatment of Pulmonary Tuberculosis: An Exploratory Proof-of-Concept Study.

Diagnostics (Basel, Switzerland)·2026
Same author

A Method for Workout Video Classification via Explainable and Federated Learning.

Bioengineering (Basel, Switzerland)·2026
Same author

Pancreatic cystic lesions: position paper of the SIRM-AISP multidisciplinary group.

La Radiologia medica·2026
Same author

The Italian Unitary Society of Colon-Proctology (SIUCP: Società Italiana Unitaria di Colonproctologia) guidelines for the management of obstructed and ineffective defecation syndrome.

Annals of coloproctology·2026
Same author

Abbreviated non-contrast magnetic resonance enterography for Crohn's disease: a reliability study.

La Radiologia medica·2026

Related Experiment Video

Updated: Dec 8, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.2K

Radiomics for Gleason Score Detection through Deep Learning.

Luca Brunese1, Francesco Mercaldo1,2, Alfonso Reginelli3

  • 1Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, 86100 Campobasso, Italy.

Sensors (Basel, Switzerland)
|September 24, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning model for automatically assigning Gleason scores to prostate cancer MRI scans. The AI achieved high accuracy, aiding in cancer staging and diagnosis.

Keywords:
cancerdeep learningprostateradiomic

More Related Videos

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

7.2K
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

962

Related Experiment Videos

Last Updated: Dec 8, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.2K
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

7.2K
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

962

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Prostate cancer staging relies on Gleason scores, typically assigned by radiologists.
  • Accurate Gleason scoring is crucial for determining appropriate treatment strategies.

Purpose of the Study:

  • To develop a deep learning architecture for automated Gleason score assignment from MRI.
  • To improve the efficiency and consistency of prostate cancer diagnosis.

Main Methods:

  • A deep convolutional neural network was designed to analyze MRI scans.
  • The model utilized 71 radiomic features from five categories (First Order, Shape, GLCM, GLRLM, GLSZM).
  • Data was sourced from two independent, publicly available research datasets.

Main Results:

  • The proposed deep architecture achieved high accuracy in predicting Gleason scores.
  • Accuracy ranged from 0.96 to 0.98, demonstrating the model's effectiveness.
  • The system successfully integrated radiomic features for enhanced prediction.

Conclusions:

  • The developed deep learning model shows significant promise for automated Gleason scoring in prostate cancer.
  • This AI-driven approach can assist radiologists in accurate cancer staging.
  • The study highlights the potential of radiomics and deep learning in oncological imaging analysis.