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

Machine learning-based identification of basement membrane-related signature to predict recurrence and immunotherapy benefit in bladder cancer.

Immunologic research·2026

Same author

Near-Infrared Responsive Property and Nanozyme Effect-Mediated 3D-Printed Gradient Bioactive Scaffold for Intelligent Immunomodulation in Regeneration of Large Segmental Bone Defects.

Small (Weinheim an der Bergstrasse, Germany)·2026

Same author

Data-driven closed-loop decision support system for renewable energy management in smart grids with integrated forecasting, optimization, and storage modeling.

Scientific reports·2026

Same author

Association between extreme temperature, extreme relative humidity, and biological aging: a longitudinal study.

International journal of biometeorology·2026

Same author

Expanding access to cerebrovascular imaging with routine MRI for pre-surgical assessment of intracranial tumor patients: an AI model development and multi-center validation study.

BMC medicine·2026

Same author

A multistage drug delivery system <i>via</i> 3D-printed personalized TPU scaffold and microfluidic microspheres for nurse-led outpatient treatment of endometrial cancer.

Journal of materials chemistry. B·2026

Search research articles

Related Experiment Video

Updated: Oct 14, 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

MRI-Based Machine Learning in Differentiation Between Benign and Malignant Breast Lesions.

Yanjie Zhao¹, Rong Chen², Ting Zhang¹

¹Department of Biotherapy, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China.

Frontiers in Oncology

|November 4, 2021

Summary

Machine learning and MRI texture analysis show promise in differentiating benign from malignant breast lesions. Combining Linear Discriminant Analysis (LDA) with Gradient Boosting Decision Trees (GBDT) achieved high accuracy, aiding diagnosis.

Keywords:

MRI breast lesion differential diagnosis linear discriminant analysis machine learning texture analysis

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

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Related Experiment Videos

Last Updated: Oct 14, 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

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Area of Science:

Radiology
Medical Imaging
Machine Learning

Background:

Accurate differentiation between benign and malignant breast lesions is critical for patient management.
Texture analysis and machine learning offer potential advancements in breast lesion diagnosis.

Purpose of the Study:

To evaluate the efficacy of MRI texture analysis combined with machine learning algorithms for distinguishing benign from malignant breast lesions.

Main Methods:

265 patients with breast lesions underwent MRI; texture features were extracted from contrast-enhanced T1-weighted images.
Five feature selection methods and five Linear Discriminant Analysis (LDA) based classification models were employed.
Models included Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and others.

Main Results:

All models demonstrated strong performance in discriminating lesion types, with Area Under the Curve (AUC) > 0.830.
The LDA + GBDT model showed superior discrimination, achieving 0.922 AUC in the training group.
LDA + RF also yielded promising results with a 0.906 AUC.

Conclusions:

MRI texture analysis integrated with LDA algorithms can effectively differentiate benign from malignant breast lesions.
Further multicenter research is recommended to validate these preliminary findings.