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Information from Searching Content with an Ontology-Utilizing Toolkit (iSCOUT)

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Abstract

Radiology reports are permanent legal documents that serve as official interpretation of imaging tests. Manual analysis of textual information contained in these reports requires significant time and effort. This study describes the development and initial evaluation of a toolkit that enables automated identification of relevant information from within these largely unstructured text reports. We developed and made publicly available a natural language processing toolkit, Information from Searching Content with an Ontology-Utilizing Toolkit (iSCOUT). Core functions are included in the following modules: the Data Loader, Header Extractor, Terminology Interface, Reviewer, and Analyzer. The toolkit enables search for specific terms and retrieval of (radiology) reports containing exact term matches as well as similar or synonymous term matches within the text of the report. The Terminology Interface is the main component of the toolkit. It allows query expansion based on synonyms from a controlled terminology (e.g., RadLex or National Cancer Institute Thesaurus [NCIT]). We evaluated iSCOUT document retrieval of radiology reports that contained liver cysts, and compared precision and recall with and without using NCIT synonyms for query expansion. iSCOUT retrieved radiology reports with documented liver cysts with a precision of 0.92 and recall of 0.96, utilizing NCIT. This recall (i.e., utilizing the Terminology Interface) is significantly better than using each of two search terms alone (0.72, p = 0.03 for liver cyst and 0.52, p = 0.0002 for hepatic cyst). iSCOUT reliably assembled relevant radiology reports for a cohort of patients with liver cysts with significant improvement in document retrieval when utilizing controlled lexicons.

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Acknowledgments

This work was partly funded by AHRQ grant 1R18HS019635.

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Authors and Affiliations

  1. Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA

    Ronilda Lacson, Katherine P. Andriole, Luciano M. Prevedello & Ramin Khorasani

  2. 20 Kent Street, Rm. 260A, Brookline, MA, 02445, USA

    Ronilda Lacson

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  1. Ronilda Lacson
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  2. Katherine P. Andriole
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  3. Luciano M. Prevedello
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  4. Ramin Khorasani
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Correspondence to Ronilda Lacson.

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Lacson, R., Andriole, K.P., Prevedello, L.M. et al. Information from Searching Content with an Ontology-Utilizing Toolkit (iSCOUT). J Digit Imaging 25, 512–519 (2012). https://doi.org/10.1007/s10278-012-9463-9

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