Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Magnetic Resonance Imaging

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...

You might also read

Related Articles

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

Sort by

Same author

[<sup>18</sup>F]FDG PET/CT Radiomics for Predicting Pathological Risk Subtypes of Thymic Epithelial Tumors: A Bicentric Study.

Cancers·2026

Same author

Diagnostic performance of abbreviated MRI in surveillance of patients with hepatocellular carcinoma candidates for liver transplantation.

La Radiologia medica·2026

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

Exploratory PET/CT Radiomics for Predicting Early Progression in Locally Advanced Pancreatic Cancer.

Diagnostics (Basel, Switzerland)·2026

Same author

The MRI two-perfusion IVIM model improves understanding of SARS-CoV-2 virus impact on placental tissue.

Magma (New York, N.Y.)·2026

Same author

Diagnostic performance of native T1 in grading histologically proven myocardial glycosphingolipid accumulation in Fabry disease.

Insights into imaging·2026

Search research articles

Related Experiment Video

Updated: Aug 15, 2025

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

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

Published on: December 15, 2014

3T-MRI Artificial Intelligence in Patients with Invasive Breast Cancer to Predict Distant Metastasis Status: A Pilot

Alessandro Calabrese¹, Domiziana Santucci^2,3, Michela Gravina⁴

¹Department of Radiology, University of Rome "Sapienza", Viale del Policlinico 155, 00161 Roma, Italy.

|January 8, 2023

Summary

Deep learning models show limited success in predicting breast cancer metastasis risk using MRI. Further research is needed to improve the accuracy of these advanced diagnostic tools for early breast cancer patients.

Keywords:

3T-MRI Dynamic Contrast-Enhanced sequences (DCE)Deep Learning (DL)breast cancer convolution neural network (CNN)metastasis

More Related Videos

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

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

Published on: August 16, 2020

Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model

Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model

Published on: October 2, 2020

Related Experiment Videos

Last Updated: Aug 15, 2025

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

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

Published on: December 15, 2014

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

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

Published on: August 16, 2020

Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model

Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model

Published on: October 2, 2020

Area of Science:

Radiology and Medical Imaging
Artificial Intelligence in Medicine
Oncology

Background:

Breast cancer metastasis remains a significant cause of mortality despite decreasing incidence.
A substantial percentage of early-stage breast cancer patients succumb to distant metastases.

Purpose of the Study:

To assess the efficacy of a Deep Learning Convolutional Neural Network (CNN) model in predicting distant metastasis risk.
Utilize 3T-MRI Dynamic Contrast-Enhanced (DCE) sequences for metastasis prediction.

Main Methods:

Retrospective analysis of 157 breast cancer patients' 3T-MRI DCE examinations.
Application of a Voxel Based (VB) NET CNN model with a single variable size bounding box (SVB) option.
Evaluation of CNN performance using accuracy, sensitivity, specificity, and Area Under the ROC Curve (AUC).

Main Results:

The VB-NET CNN model achieved 52.50% sensitivity, 80.51% specificity, 73.42% accuracy, and 68.56% AUC.
Identified significant correlations between distant metastasis risk and tumor size, PgR, and HER2 expression.

Conclusions:

The current Deep Learning approach demonstrates insufficient ability to predict distant metastasis in breast cancer patients.
Further development is required to enhance the predictive performance of CNNs for breast cancer metastasis.