Structured reporting is a favorable and sustainable form of reporting in radiology. Among its advantages are better presentation, clearer nomenclature, and higher quality. By using MRRT-compliant templates, the content of the categorized items (e.g., select fields) can be automatically stored in a database, which allows further research and quality analytics based on established ontologies like RadLex® linked to the items. Additionally, it is relevant to provide free-text input for descriptions of findings and impressions in complex imaging studies or for the information included with the clinical referral. So far, however, this unstructured content cannot be categorized. We developed a solution to analyze and code these free-text parts of the templates in our MRRT-compliant reporting platform, using natural language processing (NLP) with RadLex® terms in addition to the already categorized items. The established hybrid reporting concept is working successfully. The NLP tool provides RadLex® codes with modifiers (affirmed, speculated, negated). Radiologists can confirm or reject codes provided by NLP before finalizing the structured report. Furthermore, users can suggest RadLex® codes from free text that is not correctly coded with NLP or can suggest to change the modifier. Analyzing free-text fields took 1.23 s on average. Hybrid reporting enables coding of free-text information in our MRRT-compliant templates and thus increases the amount of categorized data that can be stored in the database. This enhances the possibilities for further analyses, such as correlating clinical information with radiological findings or storing high-quality structured information for machine-learning approaches.
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This project received funding from the Euratom research and training programme, 2014–2018, under grant agreement No 755523.
Benedikt Kämpgen is an employee of the Empolis Information Management GmbH (Kaiserslautern, Germany).
This article does not contain any studies with human participants or animals performed by any of the authors.
This retrospective, single-site, controlled cohort study did not need professional legal advice by the Institutional Review Board or informed consent of patients according to the state hospital law. All analyzed patient data were fully de-identified.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Jungmann, F., Arnhold, G., Kämpgen, B. et al. A Hybrid Reporting Platform for Extended RadLex Coding Combining Structured Reporting Templates and Natural Language Processing. J Digit Imaging 33, 1026–1033 (2020). https://doi.org/10.1007/s10278-020-00342-0
- Structured reporting
- Natural language processing
- Medical informatics