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. 2021 Jun 3;21(1):179.
doi: 10.1186/s12911-021-01533-7.

A systematic review of natural language processing applied to radiology reports

Arlene Casey  1 , Emma Davidson  2 , Michael Poon  2 , Hang Dong  3   4 , Daniel Duma  5 , Andreas Grivas  6 , Claire Grover  6 , Víctor Suárez-Paniagua  3   4 , Richard Tobin  6 , William Whiteley  2   7 , Honghan Wu  4   8 , Beatrice Alex  5   9
Affiliations

A systematic review of natural language processing applied to radiology reports

Arlene Casey et al. BMC Med Inform Decis Mak. .

Abstract

Background: Natural language processing (NLP) has a significant role in advancing healthcare and has been found to be key in extracting structured information from radiology reports. Understanding recent developments in NLP application to radiology is of significance but recent reviews on this are limited. This study systematically assesses and quantifies recent literature in NLP applied to radiology reports.

Methods: We conduct an automated literature search yielding 4836 results using automated filtering, metadata enriching steps and citation search combined with manual review. Our analysis is based on 21 variables including radiology characteristics, NLP methodology, performance, study, and clinical application characteristics.

Results: We present a comprehensive analysis of the 164 publications retrieved with publications in 2019 almost triple those in 2015. Each publication is categorised into one of 6 clinical application categories. Deep learning use increases in the period but conventional machine learning approaches are still prevalent. Deep learning remains challenged when data is scarce and there is little evidence of adoption into clinical practice. Despite 17% of studies reporting greater than 0.85 F1 scores, it is hard to comparatively evaluate these approaches given that most of them use different datasets. Only 14 studies made their data and 15 their code available with 10 externally validating results.

Conclusions: Automated understanding of clinical narratives of the radiology reports has the potential to enhance the healthcare process and we show that research in this field continues to grow. Reproducibility and explainability of models are important if the domain is to move applications into clinical use. More could be done to share code enabling validation of methods on different institutional data and to reduce heterogeneity in reporting of study properties allowing inter-study comparisons. Our results have significance for researchers in the field providing a systematic synthesis of existing work to build on, identify gaps, opportunities for collaboration and avoid duplication.

Keywords: Natural language processing; Radiology; Systematic review.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
PRISMA diagram for search publication retrieval
Fig. 2
Fig. 2
Clinical application of publication by year
Fig. 3
Fig. 3
NLP method breakdown
Fig. 4
Fig. 4
NLP method by year
Fig. 5
Fig. 5
Application Category and NLP Method, Mean and Median Summaries. Mean value is indicated by a vertical bar, the box shows error bars and the asterisk is the median value
See this image and copyright information in PMC

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