A Bayesian Derived Network of Breast Pathology Co-Occurrence

| February 6, 2008

Maskery, S.M., Hu, H., Hooke, J., Shriver, C.D., Liebman, M.N., A Bayesian Derived Network of Breast Pathology Co-Occurrence

Abstract

In this paper we present the validation and verification of a machine-learning based Bayesian

network of breast pathology co-occurrence. The present/not present occurrences of 29 common

breast pathologies from 1631 pathology reports were used to build the network. All pathology

reports were developed by a single pathologist. The resulting network has 25 diagnosis nodes

interconnected by 40 arcs. Each arc represents a predicted co-occurrence or null co-occurrence.

Model verification involved assessing the robustness of the original network structure after

random exclusion of 25%, 50%, and 75% of the pathology report dataset. The structure of the

network appears stable as random removal of 75% of the records in the original dataset leaves

81% of the original network intact. Model validation was primarily assessed by review of the

breast pathology literature for each arc in the network. Almost all network identified cooccurrences

(95%) have been published in the breast pathology literature or were verified by

expert opinion. In conclusion, the Bayesian network of breast pathology co-occurrence

presented here is both robust with respect to incomplete data and validated by consistency with

the breast pathology literature and by expert opinion. Further, the ability to utilize a specific

pathology observation to predict multiple co-current pathologies enables exploration of

pathology co-occurrence patterns in an intuitive manner that may have broader application in

both the breast pathologist clinical community and the breast cancer research community.

Corresponding author: Susan Maskery, Windber Research Institute, 620 7th Street, Windber, PA 15963 s.maskery@wriwindber.org

Journal of Biomedical Informatics (2007), doi: 10.1016/j.jbi. 2007.12.005


Category: Publications

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