Co-occurrence analysis for discovery of novel breast cancer pathology patterns.
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.
Maskery
Susan M
SM
Windber Research Institute, PA 15905, USA. s.maskery@wriwindber.org
Zhang
Yonghong
Y
Jordan
Rick M
RM
Hu
Hai
H
Hooke
Jeffrey A
JA
Shriver
Craig D
CD
Liebman
Michael N
MN
D000465:Algorithms; D001185:Artificial Intelligence; D001706:Biopsy / Q000379:methods*; D001943:Breast Neoplasms / Q000145:classification / Q000473:pathology*; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D014584:User-Computer Interface