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Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods.

Juan Shan1, S Kaisar Alam2, Brian Garra3

  • 1Department of Computer Science, Seidenberg School of Computer Science and Information Systems, Pace University, New York, New York, USA.

Ultrasound in Medicine & Biology
|January 26, 2016
PubMed
Summary

This study developed a computer-aided diagnosis (CAD) system for breast ultrasound using Breast Imaging Reporting and Data System (BI-RADS) features. Machine learning models achieved high accuracy in distinguishing benign from malignant lesions.

Keywords:
BI-RADSBreast Imaging Reporting and Data SystemBreast cancerComputer-aided diagnosisComputerized featuresMachine learningReceiver operating characteristicTissue characterizationTumor classificationUltrasonic imaging

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Breast ultrasound is crucial for lesion characterization.
  • Accurate differentiation of benign and malignant breast lesions remains a challenge.
  • The Breast Imaging Reporting and Data System (BI-RADS) provides standardized descriptors for breast imaging findings.

Purpose of the Study:

  • To identify effective computable features from BI-RADS for breast ultrasound.
  • To develop and evaluate a computer-aided diagnosis (CAD) system for breast ultrasound.
  • To assess the performance of different machine learning methods in classifying breast lesions.

Main Methods:

  • A database of 283 pathology-proven breast lesions was utilized.
  • Computable features based on ultrasound BI-RADS categories were designed.
  • Machine learning algorithms including decision tree, artificial neural network, random forest, and support vector machine were employed.
  • A "bottom-up" feature selection approach and 10-fold cross-validation were used.

Main Results:

  • The highest area under the receiver operating characteristic (ROC) curve (AUC) achieved was 0.84 with a support vector machine (SVM), yielding 77.7% overall accuracy.
  • The highest overall accuracy of 78.5% was obtained using a random forest classifier, with an AUC of 0.83.
  • Lesion margin and orientation were identified as optimal features, consistently effective across different machine learning methods.

Conclusions:

  • The developed CAD system effectively utilizes BI-RADS features for breast ultrasound analysis.
  • Machine learning models, particularly SVM and random forest, demonstrate strong performance in differentiating benign and malignant breast lesions.
  • Identified optimal features like lesion margin and orientation can enhance CAD system capabilities for clinical decision support.