title: Prediction of near-term risk of developing breast cancer using computerized features from bilateral mammograms.
abstract: Asymmetry of bilateral mammographic tissue density and patterns is a potentially strong indicator of having or developing breast abnormalities or early cancers. The purpose of this study is to design and test the global asymmetry features from bilateral mammograms to predict the near-term risk of women developing detectable high risk breast lesions or cancer in the next sequential screening mammography examination. The image dataset includes mammograms acquired from 90 women who underwent routine screening examinations, all interpreted as negative and not recalled by the radiologists during the original screening procedures. A computerized breast cancer risk analysis scheme using four image processing modules, including image preprocessing, suspicious region segmentation, image feature extraction, and classification was designed to detect and compute image feature asymmetry between the left and right breasts imaged on the mammograms. The highest computed area under curve (AUC) is 0.754±0.024 when applying the new computerized aided diagnosis (CAD) scheme to our testing dataset. The positive predictive value and the negative predictive value were 0.58 and 0.80, respectively.
authors.affiliation: Department of Electrical and Computer Engineering, University of Texas, El Paso, TX, United States.
authors.forename: Wenqing
authors.initials: W
authors.lastname: Sun
authors.affiliation: School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States.
authors.forename: Bin
authors.initials: B
authors.lastname: Zheng
authors.affiliation: Department of Electrical and Computer Engineering, University of Texas, El Paso, TX, United States.
authors.forename: Fleming
authors.initials: F
authors.lastname: Lure
authors.affiliation: School of Computing, Informatics, Decision Systems Engineering, Arizona State University, AZ, United States; Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China.
authors.forename: Teresa
authors.initials: T
authors.lastname: Wu
authors.affiliation: Department of Biology, University of Texas, El Paso, TX, United States.
authors.forename: Jianying
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Radiology Department, Sierra Providence Health Network, El Paso, TX, United States.
authors.forename: Benjamin Y
authors.initials: BY
authors.lastname: Wang
authors.affiliation: University Breast Care Center at the Texas Tech University Health Sciences, El Paso, TX, United States.
authors.forename: Edward C
authors.initials: EC
authors.lastname: Saltzstein
authors.affiliation: Department of Electrical and Computer Engineering, University of Texas, El Paso, TX, United States; Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China. Electronic address: wqian@utep.edu.
authors.forename: Wei
authors.initials: W
authors.lastname: Qian
mesh_terms: D000071060:Breast Density; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D042361:Mammary Glands, Human / Q000002:abnormalities; D008327:Mammography / Q000379:methods*; D011237:Predictive Value of Tests; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D018570:Risk Assessment
keywords: Bilateral mammographic asymmetry feature; Breast cancer; Computerized breast cancer risk analysis; Mammogram; Near-term breast cancer risk assessment