Development of automated detection of radiology reports citing adrenal findings

Jason Zopf; Jessica Langer; William Boonn; Woojin Kim; Hanna Zafar

doi:10.1117/12.881252

24 March 2011 Development of automated detection of radiology reports citing adrenal findings

Jason Zopf, Jessica Langer, William Boonn, Woojin Kim, Hanna Zafar

Proceedings Volume 7967, Medical Imaging 2011: Advanced PACS-based Imaging Informatics and Therapeutic Applications; 79670A (2011) https://doi.org/10.1117/12.881252
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

Abstract

Indeterminate incidental findings pose a challenge to both the radiologist and the ordering physician as their imaging appearance is potentially harmful but their clinical significance and optimal management is unknown. We seek to determine if it is possible to automate detection of adrenal nodules, an indeterminate incidental finding, on imaging examinations at our institution. Using PRESTO (Pathology-Radiology Enterprise Search tool), a newly developed search engine at our institution that mines dictated radiology reports, we searched for phrases used by attendings to describe incidental adrenal findings. Using these phrases as a guide, we designed a query that can be used with the PRESTO index. The results were refined using a modified version of NegEx to eliminate query terms that have been negated within the report text. In order to validate these findings we used an online random date generator to select two random weeks. We queried our RIS database for all reports created on those dates and manually reviewed each report to check for adrenal incidental findings. This survey produced a ground- truth dataset of reports citing adrenal incidental findings against which to compare query performance. We further reviewed the false positives and negatives identified by our validation study, in an attempt to improve the performance query. This algorithm is an important step towards automating the detection of incidental adrenal nodules on cross sectional imaging at our institution. Subsequently, this query can be combined with electronic medical record data searches to determine the clinical significance of these findings through resultant follow-up.

Citation Download Citation

Jason Zopf, Jessica Langer, William Boonn, Woojin Kim, and Hanna Zafar "Development of automated detection of radiology reports citing adrenal findings", Proc. SPIE 7967, Medical Imaging 2011: Advanced PACS-based Imaging Informatics and Therapeutic Applications, 79670A (24 March 2011); https://doi.org/10.1117/12.881252

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KEYWORDS

Radiology

Databases

Kidney

Detection and tracking algorithms

Detector development

Computed tomography

Genetic algorithms

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