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Proceedings Paper

Cluster analysis of BI-RADS descriptions of biopsy-proven breast lesions
Author(s): Mia K. Markey; Joseph Y. Lo; Georgia D. Tourassi; Carey E. Floyd Jr.
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Paper 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. Agglomerative hierarchical clustering and k-means clustering were used to identify clusters in a large, heterogeneous computer-aided diagnosis database based on mammographic findings (BI-RADS) and patient age. The clusters were examined in terms of their feature distributions. The clusters showed logical separation of distinct clinical subtypes such as architectural distortions, masses, and calcifications. Moreover, the common subtypes of masses and calcifications were stratified into clusters based on age groupings. The percent of the cases that were malignant was notably different among the clusters. Cluster analysis can provide a powerful tool in discerning the subgroups present in a large, heterogeneous computer-aided diagnosis database.

Paper Details

Date Published: 9 May 2002
PDF: 8 pages
Proc. SPIE 4684, Medical Imaging 2002: Image Processing, (9 May 2002); doi: 10.1117/12.467177
Show Author Affiliations
Mia K. Markey, Duke Univ. and Duke Univ. Medical Ctr. (United States)
Joseph Y. Lo, Duke Univ. and Duke Univ. Medical Ctr. (United States)
Georgia D. Tourassi, Duke Univ. Medical Ctr. (United States)
Carey E. Floyd Jr., Duke Univ. and Duke Univ. Medical Ctr. (United States)

Published in SPIE Proceedings Vol. 4684:
Medical Imaging 2002: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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