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 Experiment Video

Updated: Feb 24, 2026

Clinical Imaging of Microwave Mammography
05:28

Clinical Imaging of Microwave Mammography

Published on: November 14, 2025

303

Breast Tumor Classification Based on a Computerized Breast Imaging Reporting and Data System Feature System.

Mengyun Qiao1, Yuzhou Hu1, Yi Guo1

  • 1Department of Electronic Engineering, Fudan University, Shanghai, China.

Journal of Ultrasound in Medicine : Official Journal of the American Institute of Ultrasound in Medicine
|August 15, 2017
PubMed
Summary

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

Disruption of the <i>UPC2</i> Gene Enhances Fluconazole Antifungal Activity by Inhibiting <i>HAC1</i> mRNA Splicing in <i>Candida albicans</i>.

Pathogens (Basel, Switzerland)·2026
Same author

Generative adversarial networks-based Image-to-Image translation allows tumor consistency prediction from standard MR images in pituitary adenomas.

PLOS digital health·2026
Same author

Multimodal Fusion of 3D CT and Pathological Images for Gastric Cancer Recurrence Prediction.

Cancer management and research·2026
Same author

SSUH2 Promotes Odontogenic Differentiation of SCAPs via the FOXM1/PDK1-Mediated Regulation of Mitochondrial Function.

International endodontic journal·2026
Same author

Masked pre-training and pseudo-vessel enhancement-based semi-supervised cerebral vascular segmentation.

Medical physics·2026
Same author

Clinic-aligned Dual Distillation of Video and Image Foundation Models for Automated Breast Cancer US Diagnosis.

Radiology. Artificial intelligence·2026

This study introduces a novel digital feature system for the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) to enhance ultrasound breast cancer diagnosis accuracy.

Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Oncology
  • Radiology Informatics

Background:

  • Accurate breast cancer diagnosis relies on detailed imaging feature analysis.
  • The American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) provides a standardized framework.
  • Improving diagnostic accuracy in ultrasound breast cancer detection remains a critical clinical need.

Purpose of the Study:

  • To develop and validate novel digital high-throughput features for BI-RADS.
  • To enhance the accuracy of ultrasound breast cancer diagnosis using these features.
  • To provide a comprehensive description of BI-RADS categories through advanced feature extraction.

Main Methods:

  • Automated tumor segmentation using the phase congruency approach.
Keywords:
Breast Imaging Reporting and Data Systembenign and malignant tumor classificationbreastbreast cancerdigital high-throughput features

More Related Videos

Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model
08:32

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

Published on: October 2, 2020

7.0K
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

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

Published on: December 15, 2014

23.3K

Related Experiment Videos

Last Updated: Feb 24, 2026

Clinical Imaging of Microwave Mammography
05:28

Clinical Imaging of Microwave Mammography

Published on: November 14, 2025

303
Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model
08:32

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

Published on: October 2, 2020

7.0K
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

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

Published on: December 15, 2014

23.3K
  • Design and extraction of high-throughput features based on BI-RADS categories.
  • Feature selection via Student t test and genetic algorithm, followed by AdaBoost classification.

    • Main Results:

    • Experiments on 138 pathologically proven breast tumors demonstrated superior performance.
    • Achieved highest overall accuracy of 93.48%, sensitivity 94.20%, specificity 92.75%, and AUC 95.67%.
    • Outperformed 6 state-of-the-art BI-RADS feature extraction methods in comparative analysis.

    Conclusions:

    • The developed computerized BI-RADS feature system significantly aids radiologists in breast cancer detection.
    • The system offers enhanced guidance for making final diagnostic decisions.
    • Validated digital features improve the precision and reliability of ultrasound-based breast cancer diagnosis.