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A Vector Machine Formulation with Application to the Computer-Aided Diagnosis of Breast Cancer from DCE-MRI Screening Examinations.

This study investigates the use of a proposed vector machine formulation with application to dynamic contrast-enhanced magnetic resonance imaging examinations in the context of the computer-aided diagnosis of breast cancer. This paper describes a method for generating feature measurements that chara...

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Published in:Journal of Digital Imaging Vol. 27; no. 1; pp. 145 - 152
Main Authors: Levman, Jacob, Warner, Ellen, Causer, Petrina, Martel, Anne
Format: diagnostic images equations & formulas research tables/charts Journal Article
Published: Springer Nature Feb2014
Online Access:View this record in EBSCOhost
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      dt: Feb2014
      vid: 27
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      pub: Springer Nature
      place: New York, New York
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        atl: A Vector Machine Formulation with Application to the Computer-Aided Diagnosis of Breast Cancer from DCE-MRI Screening Examinations.
      aug:
        au:
          Levman, Jacob
          Warner, Ellen
          Causer, Petrina
          Martel, Anne
        affil: Institute of Biomedical Engineering, Department of Engineering Science, Old Road Campus Research Building, University of Oxford, Headington Oxford OX1 3PJ UK
      sug:
        subj:
          Breast Neoplasms Diagnosis
          Diagnosis, Computer Assisted
          Magnetic Resonance Imaging Methods
          Cancer Screening Methods
          Radiographic Image Interpretation, Computer-Assisted Methods
          Radiographic Image Enhancement Methods
          Breast Radiography
          Artificial Intelligence
          Genes, BRCA
          Contrast Media
          Validation Studies
          ROC Curve
          Wilcoxon Signed Rank Test
          Female
          Human
          Funding Source
          Female
      ab: This study investigates the use of a proposed vector machine formulation with application to dynamic contrast-enhanced magnetic resonance imaging examinations in the context of the computer-aided diagnosis of breast cancer. This paper describes a method for generating feature measurements that characterize a lesion's vascular heterogeneity as well as a supervised learning formulation that represents an improvement over the conventional support vector machine in this application. Spatially varying signal-intensity measures were extracted from the examinations using principal components analysis and the machine learning technique known as the support vector machine (SVM) was used to classify the results. An alternative vector machine formulation was found to improve on the results produced by the established SVM in randomized bootstrap validation trials, yielding a receiver-operating characteristic curve area of 0.82 which represents a statistically significant improvement over the SVM technique in this application.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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