Towards data-driven medical imaging using natural language processing in patients with suspected urolithiasis

Abstract

Objective: The majority of radiological reports are still written as free text and lack structure. Further evaluation of free-text reports is difficult to achieve without a great deal of manual effort, and is not possible in everyday clinical practice. This study aims to automatically capture clinical information and positive hit rates from narrative radiological reports of suspected urolithiasis using natural language processing (NLP).

Methods: Narrative reports of low dose computed tomography (CT) of the retroperitoneum from April 2016 to July 2018 (n = 1714) were analyzed using NLP. These free-text reports were automatically structured based on RadLex concepts. Manual feedback was used to test and train the NLP engine to further enhance the performance. The chi-squared test, phi coefficient, and logistic regression analysis were performed to determine the effect of clinical information on the positive hit rate of urolithiasis.

Results: Urolithiasis was affirmed in 72 % of the reports; in 38 % at least one stone was described in the kidneys, and in 45 % at least one stone was described in the ureter. Clinical information, such as previous stone history and obstructive uropathy, showed a strong correlation with confirmed urolithiasis (p = 0.001). Previous stone history and the combination of obstructive uropathy and loin pain had the highest association with positive urolithiasis (p < 0.001).

Conclusion: Applying this NLP approach to already existing free-text reports allows the conversion of such reports into a structured form. This may be valuable for epidemiological studies, to evaluate the appropriateness of CT examinations, or to answer a variety of research questions.

Keywords: Data science; Natural language processing; RadLex; Urolithiasis.