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A case-oriented web-based training system for breast cancer diagnosis.

Qinghua Huang1, Xianhai Huang2, Longzhong Liu3

  • 1School of Electronics and Information, and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China; College of Information Engineering, Shenzhen University, Shenzhen 518060, China; School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China.

Computer Methods and Programs in Biomedicine
|February 12, 2018
PubMed
Summary

This study developed an online breast ultrasound training system to improve radiologist accuracy in detecting breast cancer. Early-career radiologists showed significant diagnostic improvement, aiding in earlier and more accurate breast cancer detection.

Keywords:
BI-RADSBreast ultrasound imagesComputer-aided diagnosisFeature scoringMedical databaseWeb-based training

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Area of Science:

  • Radiology
  • Medical Education
  • Oncology

Background:

  • Breast cancer remains a leading cause of mortality in women globally.
  • Accurate and timely diagnosis of breast tumors is crucial for effective treatment.
  • Inexperienced radiologists often require specialized training for interpreting breast ultrasound images.

Purpose of the Study:

  • To develop a web-based breast ultrasound database for training purposes.
  • To provide computer-assisted diagnostic information for breast tumor detection and classification.
  • To enhance the diagnostic capabilities of radiologists using breast ultrasound imaging.

Main Methods:

  • A web database storing breast ultrasound images and diagnostic information was created.
  • A training system utilizing a feature scoring scheme based on the Breast Imaging Reporting and Data System (BI-RADS) US lexicon was designed.
  • A computer-aided diagnosis (CAD) subsystem was developed to assist radiologists in scoring BI-RADS features.

Main Results:

  • The online training system includes 1669 scored cases (412 benign, 1257 malignant).
  • Interns and junior radiologists demonstrated significant improvement in diagnosing breast tumors after training (p < .05).
  • Experienced senior radiologists showed minimal improvement (p > .05).

Conclusions:

  • The online training system effectively enhances early-career radiologists' ability to differentiate benign and malignant breast lesions.
  • The system offers a convenient and effective method for reducing breast cancer misdiagnosis.
  • Improved diagnostic skills can lead to earlier and more accurate breast cancer detection.