Co-occurrence analysis for discovery of novel breast cancer pathology patterns

Susan M. Maskery*, Yonghong Zhang, Rick M. Jordan, Hai Hu, Jeffrey A. Hooke, Craig D. Shriver, Michael N. Liebman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


To discover novel patterns in pathology co-occurrence, we have developed algorithms to analyze and visualize pathology co-occurrence. With access to a database of pathology reports, collected under a single protocol and reviewed by a single pathologist, we can conduct an analysis greater in its scope than previous studies looking at breast pathology co-occurrence. Because this data set is unique, specialized methods for pathology co-occurrence analysis and visualization are developed. Primary analysis is through a co-occurrence score based on the Jaccard coefficient. Density maps are used to visualize global co-occurrence. When our co-occurrence analysis is applied to a population stratified by menopausal status, we can successfully identify statistically significant differences in pathology co-occurrence patterns between premenopausal and postmenopausal women. Genomic and proteomic experiments are planned to discover biological mechanisms that may underpin differences seen in pathology patterns between populations.

Original languageEnglish
Pages (from-to)497-503
Number of pages7
JournalIEEE Transactions on Information Technology in Biomedicine
Issue number3
StatePublished - Jul 2006
Externally publishedYes


  • Biomedical informatics
  • Breast cancer
  • Co-occurrence analysis
  • Pathology


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