Interactive software for generation and visualization of structured findings in radiology reports

Natural Language Processing in Radiology: A Systematic Review

Radiological reporting has generated large quantities of digital content within the electronic health record, which is potentially a valuable source of information for improving clinical care and supporting research. Although radiology reports are stored for communication and documentation of diagnostic imaging, harnessing their potential requires efficient and automated information extraction: they exist mainly as free-text clinical narrative, from which it is a major challenge to obtain structured data. Natural language processing (NLP) provides techniques that aid the conversion of text into a structured representation, and thus enables computers to derive meaning from human (ie, natural language) input. Used on radiology reports, NLP techniques enable automatic identification and extraction of information. By exploring the various purposes for their use, this review examines how radiology benefits from NLP. A systematic literature search identified 67 relevant publications describing NLP methods that support practical applications in radiology. This review takes a close look at the individual studies in terms of tasks (ie, the extracted information), the NLP methodology and tools used, and their application purpose and performance results. Additionally, limitations, future challenges, and requirements for advancing NLP in radiology will be discussed.

Improved efficiency in clinical workflow of reporting measured oncology lesions via PACS-integrated lesion tracking tool

OBJECTIVE. Imaging provides evidence for the response to oncology treatment by the serial measurement of reference lesions. Unfortunately, the identification, comparison, measurement, and documentation of several reference lesions can be an inefficient process. We tested the hypothesis that optimized workflow orchestration and tight integration of a lesion tracking tool into the PACS and speech recognition system can result in improvements in oncologic lesion measurement efficiency. SUBJECTS AND METHODS. A lesion management tool tightly integrated into the PACS workflow was developed. We evaluated the effect of the use of the tool on measurement reporting time by means of a prospective time-motion study on 86 body CT examinations with 241 measureable oncologic lesions with four radiologists. RESULTS. Aggregated measurement reporting time per lesion was 11.64 seconds in standard workflow, 16.67 seconds if readers had to register measurements de novo, and 6.36 seconds for each subsequent follow-up study. Differences were statistically significant (p < 0.05) for each reader, except for one difference for one reader. CONCLUSION. Measurement reporting time can be reduced by using a PACS workflow-integrated lesion management tool, especially for patients with multiple follow-up examinations, reversing the onetime efficiency penalty at baseline registration.

Automatically correlating clinical findings and body locations in radiology reports using MedLEE

In this paper, we describe and evaluate a system that extracts clinical findings and body locations from radiology reports and correlates them. The system uses Medical Language Extraction and Encoding System (MedLEE) to map the reports' free text to structured semantic representations of their content. A lightweight reasoning engine extracts the clinical findings and body locations from MedLEE's semantic representation and correlates them. Our study is illustrative for research in which existing natural language processing software is embedded in a larger system. We manually created a standard reference based on a corpus of neuro and breast radiology reports. The standard reference was used to evaluate the precision and recall of the proposed system and its modules. Our results indicate that the precision of our system is considerably better than its recall (82.32-91.37% vs. 35.67-45.91%). We conducted an error analysis and discuss here the practical usability of the system given its recall and precision performance.

Bridging the text-image gap: a decision support tool for real-time PACS browsing

In this paper, we introduce an ontology-based technology that bridges the gap between MR images on the one hand and knowledge sources on the other hand. The proposed technology allows the user to express interest in a body region by selecting this region on the MR image he or she is viewing with a mouse device. The proposed technology infers the intended body structure from the manual selection and searches the external knowledge source for pertinent information. This technology can be used to bridge the gap between image data in the clinical workflow and (external) knowledge sources that help to assess the case with increased certainty, accuracy, and efficiency. We evaluate an instance of the proposed technology in the neurodomain by means of a user study in which three neuroradiologists participated. The user study shows that the technology has high recall (>95%) when it comes to inferring the intended brain region from the participant's manual selection. We are confident that this helps to increase the experience of browsing external knowledge sources.

A visual query-by-example image database for chest CT images: potential role as a decision and educational support tool for radiologists

Primary reading or further evaluation of diagnostic imaging examination often needs a comparison between the actual findings and the relevant prior images of the same patient or similar radiological data found in other patients. This support is of clinical importance and may have significant effects on physicians' examination reading efficiency, service-quality, and work satisfaction. We developed a visual query-by-example image database for storing and retrieving chest CT images by means of a visual browser Image Management Environment (IME) and tested its retrieval efficiency. The visual browser IME included four fundamental features (segmentation, indexing, quick load and recall, user-friendly interface) in an integrated graphical environment for a user-friendly image database management. The system was tested on a database of 2000 chest CT images, randomly chosen from the digital archives of our institutions. A sample of eight heterogeneous images were used as queries and, for each of them a team of three expert radiologists selected the most similar images from the database (a set of 15 images containing similar abnormalities in the same position of the query). The sensitivity and the positive predictive factor, both averaged over the 8 test queries and 15 answers, were respectively 0.975 and 0.91 The IME system is currently under evaluation at our institutions as an experimental application. We consider it a useful work-in-progress tool for clinical practice facilitating searches for a variety of radiological tasks.

Development of a Radiology Report Monitoring System for Case Tracking

OBJECTIVE

The objective was to develop an automated system to monitor and collect radiology reports for case-tracking purposes.

CONCLUSION

The system we developed allows users to automate the case-tracking process for either clinical follow-up or teaching purposes. With this system, radiologists can initiate the tracking of a case by dictating a keyword into the report. Any existing and future reports associated with the same patient will be collected automatically. The schematic that we developed is based on the Health Level Seven (HL7) standard, which is platform-independent. In our implementation, we used an IBM-compatible computer and commercially available software. Users can monitor the case-tracking progress from Web browsers.

Impact of pager notification on report verification times

RATIONALE AND OBJECTIVES

The purpose of this study was to assess the impact on times of verification (TOVs) by a pager notification system (PNS) that informs physicians when reports are available for signature.

MATERIALS AND METHODS

An automated PNS was implemented in the authors' department in November 2000. Monthly report verification times of each physician were collected for 3 months in the years before and after initiation of the PNS. Radiologists enrolled in the PNS and those who were not were assigned into two groups for analysis. Mean TOVs for the two sets of 3 months and for the two groups were calculated and differences recorded. Two-tailed t tests were used to assess for statistical differences between the groups.

RESULTS

Twenty-nine of 37 radiologists voluntarily enrolled in the PNS (group 1). Mean TOV was 26.75 hours (standard deviation [SD] = 17.76) for these physicians before and 14.48 hours (SD = 11.86) after the PNS was employed (P < .01). For those physicians who did not enroll in the PNS, mean TOV was 11.53 hours (SD = 5.55) before and 9.77 hours (SD = 9.86) after the PNS was employed (P = .33). Both the absolute and percentage reductions in TOVs were significantly greater for those physicians enrolled in the PNS than for those who were not (P = .035). Twenty-three of 29 (79%) physicians who used the PNS showed a reduction in their report turnaround times.

CONCLUSION

Linking the PNS with the radiology information system to notify physicians of unsigned reports was effective in reducing report verification times.