title: Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods.
abstract: This work identifies effective computable features from the Breast Imaging Reporting and Data System (BI-RADS), to develop a computer-aided diagnosis (CAD) system for breast ultrasound. Computerized features corresponding to ultrasound BI-RADs categories were designed and tested using a database of 283 pathology-proven benign and malignant lesions. Features were selected based on classification performance using a "bottom-up" approach for different machine learning methods, including decision tree, artificial neural network, random forest and support vector machine. Using 10-fold cross-validation on the database of 283 cases, the highest area under the receiver operating characteristic (ROC) curve (AUC) was 0.84 from a support vector machine with 77.7% overall accuracy; the highest overall accuracy, 78.5%, was from a random forest with the AUC 0.83. Lesion margin and orientation were optimum features common to all of the different machine learning methods. These features can be used in CAD systems to help distinguish benign from worrisome lesions.
authors.affiliation: Department of Computer Science, Seidenberg School of Computer Science and Information Systems, Pace University, New York, New York, USA. Electronic address: jshan@pace.edu.
authors.forename: Juan
authors.initials: J
authors.lastname: Shan
authors.affiliation: Improlabs Pte Ltd, Valley Point, Singapore; Computational Biomedicine Imaging and Modeling Center (CBIM), Rutgers University, Piscataway, New Jersey, USA; Department of Electrical & Electronic Engineering, Islamic University of Technology, Gazipur, Bangladesh.
authors.forename: S Kaisar
authors.initials: SK
authors.lastname: Alam
authors.affiliation: U.S. Food and Drug Administration, Silver Spring, Maryland, USA; Washington DC Veterans Affairs Medical Center, Washington, DC, USA.
authors.forename: Brian
authors.initials: B
authors.lastname: Garra
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
authors.forename: Yingtao
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Washington DC Veterans Affairs Medical Center, Washington, DC, USA.
authors.forename: Tahira
authors.initials: T
authors.lastname: Ahmed
mesh_terms: D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods / Q000592:standards*; D000069550:Machine Learning; D008954:Models, Biological; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods / Q000592:standards*; D017410:Practice Guidelines as Topic; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D016217:Ultrasonography, Mammary / Q000379:methods* / Q000592:standards*; D014481:United States
keywords: BI-RADS; Breast Imaging Reporting and Data System; Breast cancer; Computer-aided diagnosis; Computerized features; Machine learning; Receiver operating characteristic; Tissue characterization; Tumor classification; Ultrasonic imaging