Identification of Incidental Pulmonary Nodules in Free-text Radiology Reports: An Initial Investigation.

Advances in image quality produced by computed tomography (CT) and the growth in the number of image studies currently performed has made the management of incidental pulmonary nodules (IPNs) a challenging task. This research aims to identify IPNs in radiology reports of chest and abdominal CT by Natural Language Processing techiniques to recognize IPN in sentences of radiology reports. Our preliminary analysis indicates vastly different pulmonary incidental findings rates for two different patient groups.

Follow-up Recommendation Detection on Radiology Reports with Incidental Pulmonary Nodules.

The management of follow-up recommendations is fundamental for the appropriate care of patients with incidental pulmonary findings. The lack of communication of these important findings can result in important actionable information being lost in healthcare provider electronic documents. This study aims to analyze follow-up recommendations in radiology reports containing pulmonary incidental findings by using Natural Language Processing and Regular Expressions. Our evaluation highlights the different follow-up recommendation rates for oncology and non-oncology patient cohorts. The results reveal the need for a context-sensitive approach to tracking different patient cohorts in an enterprise-wide assessment.

Evaluating the Referring Physician's Clinical History and Indication as a Means for Communicating Chronic Conditions That Are Pertinent at the Point of Radiologic Interpretation.

The clinical history and indication (CHI) provided with a radiological examination are critical components of a quality interpretation by the radiologist. A patient's chronic conditions offer the context in which acute symptoms and findings can be interpreted more accurately. Seven pertinent (potentially diagnosis altering) chronic conditions, which are fairly prevalent at our institution, were selected. We analyze if and how in 140 CHIs there was mention of a patient's previously reported chronic condition and if and how the condition was subsequently described in the radiology report using a four-item scheme (Mention/Specialization, Generalization, Common comorbidity, No mention). In 40.7% of CHIs, the condition was rated Mention/Specialization. Therefore, we reject our first hypothesis that the CHI is a reliable source for obtaining pertinent chronic conditions (≥ 90.0%). Non-oncological conditions were significantly more likely rated No mention in the CHI than oncological conditions (58.7 versus 8.3%, P < 0.0001). Stat cases were significantly more frequently No mention than non-stat cases (60.0 versus 31.3%, P = 0.0134). We accept our second hypothesis that the condition's rating in the CHI is significantly correlated with its rating of the final radiology report (χ(2) test, P < 0.00001). Our study demonstrates an alarming lack of communication of pertinent medical information to the radiologist, which may negatively impact interpretation quality. Presenting automatically aggregated patient information to the radiologist may be a potential avenue for improving interpretation and adding value of the radiology department to the care chain.

A context-sensitive image annotation recommendation engine for radiology.

In the typical radiology reading workflow, a radiologist would go through an imaging study and annotate specific regions of interest. The radiologist has the option to select a suitable description (e.g., "calcification") from a list of predefined descriptions, or input the description directly as free-text. However, this process is time-consuming and the descriptions are not standardized over time, even for the same patient or the same general finding. In this paper, we describe an approach that presents finding descriptions based on textual information extracted from a patient's prior reports. Using 133 finding descriptions obtained in routine oncology workflow, we demonstrate how the system can be used to reduce keystrokes by up to 86% in about 38% of the instances. We have integrated our solution into a PACS and discuss how the system can be used in a clinical setting to improve the image annotation workflow efficiency and promote standardization of finding descriptions.

Determining scanned body part from DICOM study description for relevant prior study matching.

The typical radiology reporting workflow involves the radiologist first looking at one or more relevant prior studies before interpreting the current study. To improve workflow efficiency, PACS systems can display relevant prior imaging studies, typically based on a study's anatomy as indicated in the Body Part Examined field of the DICOM header. The content of the Body Part Examined field can be very generic. For instance, an imaging study to exclude pancreatitis and another one to exclude renal stones will both have "abdomen" in their body part field, making it difficult to differentiate them. To improve prior study matching and support better study filtering, in this paper, we present a rule-based approach to determine specific body parts contained in the free-text DICOM Study Description field. Algorithms were trained using a production dataset of 1200 randomly selected unique study descriptions and validated against a test dataset of 404 study descriptions. Our validation resulted in 99.94% accuracy. The proposed technique suggests that a rule-based approach can be used for domain specific body part extraction from DICOM headers.

Cross-sectional relatedness between sentences in breast radiology reports: development of an SVM classifier and evaluation against annotations of five breast radiologists.

Introduce the notion of cross-sectional relatedness as an informational dependence relation between sentences in the conclusion section of a breast radiology report and sentences in the findings section of the same report. Assess inter-rater agreement of breast radiologists. Develop and evaluate a support vector machine (SVM) classifier for automatically detecting cross-sectional relatedness. A standard reference is manually created from 444 breast radiology reports by the first author. A subset of 37 reports is annotated by five breast radiologists. Inter-rater agreement is computed among their annotations and standard reference. Thirteen numerical features are developed to characterize pairs of sentences; the optimal feature set is sought through forward selection. Inter-rater agreement is F-measure 0.623. SVM classifier has F-measure of 0.699 in the 12-fold cross-validation protocol against standard reference. Report length does not correlate with the classifier's performance (correlation coefficient = -0.073). SVM classifier has average F-measure of 0.505 against annotations by breast radiologists. Mediocre inter-rater agreement is possibly caused by: (1) definition is insufficiently actionable, (2) fine-grained nature of cross-sectional relatedness on sentence level, instead of, for instance, on paragraph level, and (3) higher-than-average complexity of 37-report sample. SVM classifier performs better against standard reference than against breast radiologists's annotations. This is supportive of (3). SVM's performance on standard reference is satisfactory. Since optimal feature set is not breast specific, results may transfer to non-breast anatomies. Applications include a smart report viewing environment and data mining.

Using image references in radiology reports to support enhanced report-to-image navigation.

Radiology reports frequently contain references to image slices that are illustrative of described findings, for instance, "Neurofibroma in superior right extraconal space (series 5, image 104)". In the current workflow, if a report consumer wants to view a referenced image, he or she needs to (1) open prior study, (2) open the series of interest (series 5 in this example), and (3) navigate to the corresponding image slice (image 104). This research aims to improve this report-to-image navigation process by providing hyperlinks to images. We develop and evaluate a regular expressions-based algorithm that recognizes image references at a sentence level. Validation on 314 image references from general radiology reports shows precision of 99.35%, recall of 98.08% and F-measure of 98.71%, suggesting this is a viable approach for image reference extraction. We demonstrate how recognized image references can be hyperlinked in a PACS report viewer allowing one-click access to the images.

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.

Algorithmic and user study of an autocompletion algorithm on a large medical vocabulary.

INTRODUCTION: Autocompletion supports human-computer interaction in software applications that let users enter textual data. We will be inspired by the use case in which medical professionals enter ontology concepts, catering the ongoing demand for structured and standardized data in medicine. OBJECTIVES: Goal is to give an algorithmic analysis of one particular autocompletion algorithm, called multi-prefix matching algorithm, which suggests terms whose words' prefixes contain all words in the string typed by the user, e.g., in this sense, opt ner me matches optic nerve meningioma. Second we aim to investigate how well it supports users entering concepts from a large and comprehensive medical vocabulary (snomed ct). METHODS: We give a concise description of the multi-prefix algorithm, and sketch how it can be optimized to meet required response time. Performance will be compared to a baseline algorithm, which gives suggestions that extend the string typed by the user to the right, e.g. optic nerve m gives optic nerve meningioma, but opt ner me does not. We conduct a user experiment in which 12 participants are invited to complete 40 snomed ct terms with the baseline algorithm and another set of 40 snomed ct terms with the multi-prefix algorithm. RESULTS: Our results show that users need significantly fewer keystrokes when supported by the multi-prefix algorithm than when supported by the baseline algorithm. CONCLUSIONS: The proposed algorithm is a competitive candidate for searching and retrieving terms from a large medical ontology.