A Bayesian derived network of breast pathology co-occurrence

  • Authors:

  • Susan M. Maskery;Hai Hu;Jeffrey Hooke;Craig D. Shriver;Michael N. Liebman

  • Affiliations:

  • Windber Research Institute, 620 7th Street, Windber, PA 15963, USA;Windber Research Institute, 620 7th Street, Windber, PA 15963, USA;Clinical Breast Care Project, Walter Reed Army Medical Center, 6900 Georgia Ave NW, Washington, DC, 20307, USA;Clinical Breast Care Project, Walter Reed Army Medical Center, 6900 Georgia Ave NW, Washington, DC, 20307, USA and General Surgery Service, Walter Reed Army Medical Center, 6900 Georgia Ave NW, Wa ...;Windber Research Institute, 620 7th Street, Windber, PA 15963, USA

  • Venue:
  • Journal of Biomedical Informatics
  • Year:
  • 2008

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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 co-occurrences (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.