[Basics and applications of Natural Language Processing (NLP) in radiology]

A Hybrid Reporting Platform for Extended RadLex Coding Combining Structured Reporting Templates and Natural Language Processing

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.

Development and web deployment of an automated neuroradiology MRI protocoling tool with natural language processing

Background

A systematic approach to MRI protocol assignment is essential for the efficient delivery of safe patient care. Advances in natural language processing (NLP) allow for the development of accurate automated protocol assignment. We aim to develop, evaluate, and deploy an NLP model that automates protocol assignment, given the clinician indication text.

Methods

We collected 7139 spine MRI protocols (routine or contrast) and 990 head MRI protocols (routine brain, contrast brain, or other) from a single institution. Protocols were split into training (n = 4997 for spine MRI; n = 839 for head MRI), validation (n = 1071 for spine MRI, fivefold cross-validation used for head MRI), and test (n = 1071 for spine MRI; n = 151 for head MRI) sets. fastText and XGBoost were used to develop 2 NLP models to classify spine and head MRI protocols, respectively. A Flask-based web app was developed to be deployed via Heroku.

Results

The spine MRI model had an accuracy of 83.38% and a receiver operator characteristic area under the curve (ROC-AUC) of 0.8873. The head MRI model had an accuracy of 85.43% with a routine brain protocol ROC-AUC of 0.9463 and contrast brain protocol ROC-AUC of 0.9284. Cancer, infectious, and inflammatory related keywords were associated with contrast administration. Structural anatomic abnormalities and stroke/altered mental status were indicative of routine spine and brain MRI, respectively. Error analysis revealed increasing the sample size may improve performance for head MRI protocols. A web version of the model is provided for demonstration and deployment.

Conclusion

We developed and web-deployed two NLP models that accurately predict spine and head MRI protocol assignment, which could improve radiology workflow efficiency.

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

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.

[Cross-enterprise interoperability : Challenges and principles for technical implementation]

Demographic change, the migration of medical professionals and economic constraints are leading to an increasing specialisation and concentration of resources in the healthcare sector. The digital mapping of cross-enterprise process chains between different medical care levels in turn requires digital networking and syntactical and semantical interoperability in information exchange. Cross-enterprise electronic patient records based on the Cross-Enterprise Document Sharing (XDS.b) integration profile defined by the Integrating the Healthcare Enterprise (IHE) initiative are the basis of the digital exchange of medical information between patients and service providers. Syntactical interoperability is ensured by the object definitions in the XDS Affinity domain and the Clinical Document Architecture (CDA) document format. Semantical interoperability is achieved by the use of standardized terminology and code systems. Terminology servers make it easy to deploy standard ontologies and translate proprietary code systems. New interoperability standards, e.g. based on Fast Healthcare Interoperability Resources (FHIR), are being developed to facilitate the exchange of structured medical information objects on mobile platforms in the future.

[Natural language processing in radiology : Neither trivial nor impossible]

BACKGROUND

The need for application expertise in natural language processing (NLP) is increasing in radiology. This way, in a complementary fashion to structured reporting using templates, the necessary database for quality assurance and continuous process optimization can be generated.

OBJECTIVE

Possibilities and challenges of the application of NLP from the radiology point of view are explained.

MATERIALS AND METHODS

The requirements and expectations for NLP systems are identified and demonstrated using a case study.

RESULTS

For an effective use of this technology, NLP tasks for the interpretation of text using RadLex, an intuitive usage and feedback option as well as transparent quality of the NLP results are important.

DISCUSSION

Using suitable NLP systems, targeted information can be extracted from large amounts of free text with manageable manual effort and high quality.