• About
  • Policies
  • What is openaccess
  • Library
  • Contact
Advanced search
      View Item 
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Industrial Engineering
      • View Item
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Industrial Engineering
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      A comprehensive methodology for determining the most informative mammographic features

      Thumbnail
      View / Download
      230.2 Kb
      Author
      Wu, Y.
      Alagoz O.
      Ayvaci, M.U.S.
      Munoz Del Rio, A.
      Vanness, D.J.
      Woods, R.
      Burnside, E.S.
      Date
      2013
      Source Title
      Journal of Digital Imaging
      Print ISSN
      0897-1889
      Volume
      26
      Issue
      5
      Pages
      941 - 947
      Language
      English
      Type
      Article
      Item Usage Stats
      134
      views
      92
      downloads
      Abstract
      This study aims to determine the most informative mammographic features for breast cancer diagnosis using mutual information (MI) analysis. Our Health Insurance Portability and Accountability Act-approved database consists of 44,397 consecutive structured mammography reports for 20,375 patients collected from 2005 to 2008. The reports include demographic risk factors (age, family and personal history of breast cancer, and use of hormone therapy) and mammographic features from the Breast Imaging Reporting and Data System lexicon. We calculated MI using Shannon's entropy measure for each feature with respect to the outcome (benign/malignant using a cancer registry match as reference standard). In order to evaluate the validity of the MI rankings of features, we trained and tested naïve Bayes classifiers on the feature with tenfold cross-validation, and measured the predictive ability using area under the ROC curve (AUC). We used a bootstrapping approach to assess the distributional properties of our estimates, and the DeLong method to compare AUC. Based on MI, we found that mass margins and mass shape were the most informative features for breast cancer diagnosis. Calcification morphology, mass density, and calcification distribution provided predictive information for distinguishing benign and malignant breast findings. Breast composition, associated findings, and special cases provided little information in this task. We also found that the rankings of mammographic features with MI and AUC were generally consistent. MI analysis provides a framework to determine the value of different mammographic features in the pursuit of optimal (i.e., accurate and efficient) breast cancer diagnosis. © 2013 Society for Imaging Informatics in Medicine.
      Keywords
      BI-RADS
      Breast cancer
      Decision support
      Informatics
      Mammography
      Mutual information
      BI-RADS
      Breast Cancer
      Decision supports
      Informatics
      Mutual informations
      Biomineralization
      Bone
      Decision support systems
      Diseases
      Health insurance
      Mammography
      Medical imaging
      aged
      area under the curve
      Breast Neoplasms
      calcinosis
      computer assisted diagnosis
      factual database
      female
      hospital information system
      human
      mammography
      middle aged
      procedures
      radiography
      receiver operating characteristic
      retrospective study
      risk factor
      article
      breast tumor
      computer assisted diagnosis
      mammography
      methodology
      Aged
      Area Under Curve
      Breast Neoplasms
      Calcinosis
      Databases, Factual
      Female
      Humans
      Mammography
      Middle Aged
      Radiographic Image Interpretation, Computer-Assisted
      Radiology Information Systems
      Retrospective Studies
      Risk Factors
      ROC Curve
      Aged
      Area Under Curve
      Breast Neoplasms
      Calcinosis
      Databases, Factual
      Female
      Humans
      Mammography
      Middle Aged
      Radiographic Image Interpretation, Computer-Assisted
      Radiology Information Systems
      Retrospective Studies
      Risk Factors
      ROC Curve
      Permalink
      http://hdl.handle.net/11693/20778
      Published Version (Please cite this version)
      http://dx.doi.org/10.1007/s10278-013-9588-5
      Collections
      • Department of Industrial Engineering 677
      Show full item record

      Related items

      Showing items related by title, author, creator and subject.

      • Thumbnail

        Localization of diagnostically relevant regions of interest in whole slide images: a comparative study 

        Mercan, E.; Aksoy, S.; Shapiro, L. G.; Weaver, D. L.; Brunyé, T. T.; Elmore, J. G. (Springer New York LLC, 2016-08)
        Whole slide digital imaging technology enables researchers to study pathologists’ interpretive behavior as they view digital slides and gain new understanding of the diagnostic medical decision-making process. In this ...
      • Thumbnail

        A resampling-based meta-analysis for detection of differential gene expression in breast cancer 

        Gur-Dedeoglu, B.; Konu, O.; Kir, S.; Ozturk, A. R.; Bozkurt, B.; Ergul, G.; Yulug, I.G. (BioMed Central, 2008)
        Background: Accuracy in the diagnosis of breast cancer and classification of cancer subtypes has improved over the years with the development of well-established immunohistopathological criteria. More recently, diagnostic ...
      • Thumbnail

        Detection of microcalcifications in mammograms using local maxima and adaptive wavelet transform analysis 

        Bagci, A. M.; Çetin, A. Enis (The Institution of Engineering and Technology(IET), 2002-10-24)
        A method for computer-aided diagnosis of microcalcification clusters in mammogram images is presented. Microcalcification clusters which are an early sign of breast cancer appear as isolated bright spots in mammograms. ...

      Browse

      All of BUIRCommunities & CollectionsTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsThis CollectionTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartments

      My Account

      Login

      Statistics

      View Usage StatisticsView Google Analytics Statistics

      Bilkent University

      If you have trouble accessing this page and need to request an alternate format, contact the site administrator. Phone: (312) 290 1771
      Copyright © Bilkent University - Library IT

      Contact Us | Send Feedback | Off-Campus Access | Admin | Privacy