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Journal of Digital Imaging

, Volume 30, Issue 6, pp 681–686 | Cite as

PathBot: A Radiology-Pathology Correlation Dashboard

  • Linda C. Kelahan
  • Amit D. Kalaria
  • Ross W. Filice
Article

Abstract

Pathology is considered the “gold standard” of diagnostic medicine. The importance of radiology-pathology correlation is seen in interdepartmental patient conferences such as “tumor boards” and by the tradition of radiology resident immersion in a radiologic-pathology course at the American Institute of Radiologic Pathology. In practice, consistent pathology follow-up can be difficult due to time constraints and cumbersome electronic medical records. We present a radiology-pathology correlation dashboard that presents radiologists with pathology reports matched to their dictations, for both diagnostic imaging and image-guided procedures. In creating our dashboard, we utilized the RadLex ontology and National Center for Biomedical Ontology (NCBO) Annotator to identify anatomic concepts in pathology reports that could subsequently be mapped to relevant radiology reports, providing an automated method to match related radiology and pathology reports. Radiology-pathology matches are presented to the radiologist on a web-based dashboard. We found that our algorithm was highly specific in detecting matches. Our sensitivity was slightly lower than expected and could be attributed to missing anatomy concepts in the RadLex ontology, as well as limitations in our parent term hierarchical mapping and synonym recognition algorithms. By automating radiology-pathology correlation and presenting matches in a user-friendly dashboard format, we hope to encourage pathology follow-up in clinical radiology practice for purposes of self-education and to augment peer review. We also hope to provide a tool to facilitate the production of quality teaching files, lectures, and publications. Diagnostic images have a richer educational value when they are backed up by the gold standard of pathology.

Keywords

Dashboard Medical records systems Radiology-pathology correlation Radiology teaching files Radiology workflow RadLex 

Notes

Acknowledgements

The authors would like to acknowledge and thank Kristen Kalaria for her help with manuscript preparation.

Compliance with Ethical Standards

Our study met the criteria for exemption from approval by the institutional review board at our institution.

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Copyright information

© Society for Imaging Informatics in Medicine 2017

Authors and Affiliations

  • Linda C. Kelahan
    • 1
  • Amit D. Kalaria
    • 1
  • Ross W. Filice
    • 1
    • 2
  1. 1.Department of RadiologyMedStar Georgetown University HospitalWashington, DCUSA
  2. 2.MedStar Medical Group RadiologyWashington, D.CUSA

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