Self-organizing map for cluster analysis of a breast cancer database

  • Authors:

  • Mia K. Markey;Joseph Y. Lo;Georgia D. Tourassi;Carey E. Floyd, Jr.

  • Affiliations:

  • Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA and Digital Imaging Research Division, Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA;Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA and Digital Imaging Research Division, Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA;Digital Imaging Research Division, Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA;Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA and Digital Imaging Research Division, Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA

  • Venue:
  • Artificial Intelligence in Medicine
  • Year:
  • 2003

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Abstract

The purpose of this study was to identify and characterize clusters in a heterogeneous breast cancer computer-aided diagnosis database. Identification of subgroups within the database could help elucidate clinical trends and facilitate future model building. A self-organizing map (SOM) was used to identify clusters in a large (2258 cases), heterogeneous computer-aided diagnosis database based on mammographic findings (BI-RADS(TM)) and patient age. The resulting clusters were then characterized by their prototypes determined using a constraint satisfaction neural network (CSNN). The clusters showed logical separation of clinical subtypes such as architectural distortions, masses, and calcifications. Moreover, the broad categories of masses and calcifications were stratified into several clusters (seven for masses and three for calcifications). The percent of the cases that were malignant was notably different among the clusters (ranging from 6 to 83%). A feed-forward back-propagation artificial neural network (BP-ANN) was used to identify likely benign lesions that may be candidates for follow up rather than biopsy. The performance of the BP-ANN varied considerably across the clusters identified by the SOM. In particular, a cluster (#6) of mass cases (6% malignant) was identified that accounted for 79% of the recommendations for follow up that would have been made by the BP-ANN. A classification rule based on the profile of cluster #6 performed comparably to the BP-ANN, providing approximately 25% specificity at 98% sensitivity. This performance was demonstrated to generalize to a large (2177) set of cases held-out for model validation.