Structured reporting: if, why, when, how—and at what expense? Results of a focus group meeting of radiology professionals from eight countries

Purpose: To determine why, despite growing evidence that radiologists and referring physicians prefer structured reporting (SR) to free text (FT) reporting, SR has not been widely adopted in most radiology departments. Methods: A focus group was formed consisting of 11 radiology professionals from eight countries. Eight topics were submitted for discussion. The meeting was videotaped, transcribed, and analyzed according to the principles of qualitative healthcare research. Results: Perceived advantages of SR were facilitation of research, easy comparison, discouragement of ambiguous reports, embedded links to images, highlighting important findings, not having to dictate text nobody will read, and automatic translation of teleradiology reports. Being compelled to report within a rigid frame was judged unacceptable. Personal convictions appeared to have high emotional value. It was felt that other healthcare stakeholders would impose SR without regard to what radiologists thought of it. If the industry were to provide ready-made templates for selected examinations, most radiologists would use them. Conclusion: If radiologists can be convinced of the advantages of SR and the risks associated with failing to participate actively in its implementation, they will take a positive stand. The industry should propose technology allowing SR without compromising accuracy, completeness, workflows, and cost-benefit balance

A timely computer-aided detection system for acute ischemic and hemorrhagic stroke on CT in an emergency environment

Standalone Presentations: no. LL-IN1105BACKGROUND: When a patient is accepted in the emergency room suspected of stroke, time is of the most importance. The infarct brain area suffers irreparable damage as soon as three hours after the onset of stroke symptoms. Non-contrast CT scan is the standard first line of investigation used to identify hemorrhagic stroke cases. However, CT brain images do not show hyperacute ischemia and small hemorrhage clearly and thus may be missed by emergency physicians. We reported a timely computer-aided detection (CAD) system for small hemorrhages on CT that has been successfully developed as an aid to ER physicians to help improve detection for Acute Intracranial Hemorrhage (AIH). This CAD system has been enhanced for diagnosis of acute ischemic stroke in addition to hemorrhagic stroke, which becomes a more complete and clinically useful tool for assisting emergency physicians and radiologists. In the detection algorithm, brain matter is first segmented, realigned, and left-right brain symmetry is evaluated. As in the AIH system, the system confirms hemorrhagic stroke by detecting blood presence with anatomical and medical knowledge-based criteria. For detecting ischemia, signs such as regional hypodensity, blurring of grey and white matter differentiation, effacement of cerebral sulci, and hyperdensity in middle cerebral artery, are evaluated …published_or_final_versio

A Systematic Approach for Using DICOM Structured Reports in Clinical Processes: Focus on Breast Cancer

The final publication is available at Springer via http://dx.doi.org/10.1007/s10278-014-9728-6.This paper describes a methodology for redesigning the clinical processes to manage diagnosis, follow-up, and response to treatment episodes of breast cancer. This methodology includes three fundamental elements: (1) identification of similar and contrasting cases that may be of clinical relevance based upon a target study, (2) codification of reports with standard medical terminologies, and (3) linking and indexing the structured reports obtained with different techniques in a common system. The combination of these elements should lead to improvements in the clinical management of breast cancer patients. The motivation for this work is the adaptation of the clinical processes for breast cancer created by the Valencian Community health authorities to the new techniques available for data processing. To achieve this adaptation, it was necessary to design nine Digital Imaging and Communications in Medicine (DICOM) structured report templates: six diagnosis templates and three summary templates that combine reports from clinical episodes. A prototype system is also described that links the lesion to the reports. Preliminary tests of the prototype have shown that the interoperability among the report templates allows correlating parameters from different reports. Further work is in progress to improve the methodology in order that it can be applied to clinical practice.We thank the subject matter experts for sharing their insights through this study. We are especially appreciative of the efforts of the Radiology Unit and Medical Oncology Unit teams at the University Hospital Dr. Peset. This work was partially supported by the Vicerectorat d'Investigacio de la Universitat Politecnica de Valencia (UPVLC) to develop the project "Mejora del proceso diagnostico del cancer de mama" with reference UPV-FE-2013-8.Medina, R.; Torres Serrano, E.; Segrelles Quilis, JD.; Blanquer Espert, I.; Martí Bonmatí, L.; Almenar-Cubells, D. (2015). A Systematic Approach for Using DICOM Structured Reports in Clinical Processes: Focus on Breast Cancer. Journal of Digital Imaging. 28(2):132-145. doi:10.1007/s10278-014-9728-6S132145282Ratib O: Imaging informatics: From image management to image navigation. Yearb Med Inform 2009; 167–172Oakley J. Digital Imaging: A Primer for Radiographers, Radiologists and Health Care Professionals. Cambridge University Press, 2003.Prokosch HU, Dudeck J: Hospital information systems: Design and development characteristics, impact and future architecture. Elsevier health sciences, 1995Foster I, Kesselman C, Tuecke S. 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Creation and Curation of the Society of Imaging Informatics in Medicine Hackathon Dataset

In order to support innovation, the Society of Imaging Informatics in Medicine (SIIM) elected to create a collaborative computing experience called a "hackathon." The SIIM Hackathon has always consisted of two components, the event itself and the infrastructure and resources provided to the participants. In 2014, SIIM provided a collection of servers to participants during the annual meeting. After initial server setup, it was clear that clinical and imaging "test" data were also needed in order to create useful applications. We outline the goals, thought process, and execution behind the creation and maintenance of the clinical and imaging data used to create DICOM and FHIR Hackathon resources

KneeTex: an ontology–driven system for information extraction from MRI reports

Background. In the realm of knee pathology, magnetic resonance imaging (MRI) has the advantage of visualising all structures within the knee joint, which makes it a valuable tool for increasing diagnostic accuracy and planning surgical treatments. Therefore, clinical narratives found in MRI reports convey valuable diagnostic information. A range of studies have proven the feasibility of natural language processing for information extraction from clinical narratives. However, no study focused specifically on MRI reports in relation to knee pathology, possibly due to the complexity of knee anatomy and a wide range of conditions that may be associated with different anatomical entities. In this paper we describe KneeTex, an information extraction system that operates in this domain. Methods. As an ontology–driven information extraction system, KneeTex makes active use of an ontology to strongly guide and constrain text analysis. We used automatic term recognition to facilitate the development of a domain–specific ontology with sufficient detail and coverage for text mining applications. In combination with the ontology, high regularity of the sublanguage used in knee MRI reports allowed us to model its processing by a set of sophisticated lexico–semantic rules with minimal syntactic analysis. The main processing steps involve named entity recognition combined with coordination, enumeration, ambiguity and co–reference resolution, followed by text segmentation. Ontology–based semantic typing is then used to drive the template filling process. Results. We adopted an existing ontology, TRAK (Taxonomy for RehAbilitation of Knee conditions), for use within KneeTex. The original TRAK ontology expanded from 1,292 concepts, 1,720 synonyms and 518 relationship instances to 1,621 concepts, 2,550 synonyms and 560 relationship instances. This provided KneeTex with a very fine–grained lexico–semantic knowledge base, which is highly attuned to the given sublanguage. Information extraction results were evaluated on a test set of 100 MRI reports. A gold standard consisted of 1,259 filled template records with the following slots: finding, finding qualifier, negation, certainty, anatomy and anatomy qualifier. KneeTex extracted information with precision of 98.00%, recall of 97.63% and F–measure of 97.81%, the values of which are in line with human–like performance. Conclusions. KneeTex is an open–source, stand–alone application for information extraction from narrative reports that describe an MRI scan of the knee. Given an MRI report as input, the system outputs the corresponding clinical findings in the form of JavaScript Object Notation objects. The extracted information is mapped onto TRAK, an ontology that formally models knowledge relevant for the rehabilitation of knee conditions. As a result, formally structured and coded information allows for complex searches to be conducted efficiently over the original MRI reports, thereby effectively supporting epidemiologic studies of knee conditions

KneeTex: An ontology-driven system for information extraction from MRI reports

Background. In the realm of knee pathology, magnetic resonance imaging (MRI) has the advantage of visualising all structures within the knee joint, which makes it a valuable tool for increasing diagnostic accuracy and planning surgical treatments. Therefore, clinical narratives found in MRI reports convey valuable diagnostic information. A range of studies have proven the feasibility of natural language processing for information extraction from clinical narratives. However, no study focused specifically on MRI reports in relation to knee pathology, possibly due to the complexity of knee anatomy and a wide range of conditions that may be associated with different anatomical entities. In this paper we describe KneeTex, an information extraction system that operates in this domain. Methods. As an ontology–driven information extraction system, KneeTex makes active use of an ontology to strongly guide and constrain text analysis. We used automatic term recognition to facilitate the development of a domain–specific ontology with sufficient detail and coverage for text mining applications. In combination with the ontology, high regularity of the sublanguage used in knee MRI reports allowed us to model its processing by a set of sophisticated lexico–semantic rules with minimal syntactic analysis. The main processing steps involve named entity recognition combined with coordination, enumeration, ambiguity and co–reference resolution, followed by text segmentation. Ontology–based semantic typing is then used to drive the template filling process. Results. We adopted an existing ontology, TRAK (Taxonomy for RehAbilitation of Knee conditions), for use within KneeTex. The original TRAK ontology expanded from 1,292 concepts, 1,720 synonyms and 518 relationship instances to 1,621 concepts, 2,550 synonyms and 560 relationship instances. This provided KneeTex with a very fine–grained lexico–semantic knowledge base, which is highly attuned to the given sublanguage. Information extraction results were evaluated on a test set of 100 MRI reports. A gold standard consisted of 1,259 filled template records with the following slots: finding, finding qualifier, negation, certainty, anatomy and anatomy qualifier. KneeTex extracted information with precision of 98.00%, recall of 97.63% and F–measure of 97.81%, the values of which are in line with human–like performance. Conclusions. KneeTex is an open–source, stand–alone application for information extraction from narrative reports that describe an MRI scan of the knee. Given an MRI report as input, the system outputs the corresponding clinical findings in the form of JavaScript Object Notation objects. The extracted information is mapped onto TRAK, an ontology that formally models knowledge relevant for the rehabilitation of knee conditions. As a result, formally structured and coded information allows for complex searches to be conducted efficiently over the original MRI reports, thereby effectively supporting epidemiologic studies of knee conditions

Towards an ontology-driven clinical experience sharing ecosystem: demonstration with liver cases

Past medical cases, hence clinical experience, are invaluable resources in supporting clinical practice, research, and education. Medical professionals need to be able to exchange information about patient cases and explore them from subjective perspectives. This requires a systematic and flexible methodology to case representation for supporting the exchange of processable patient information. We present an ontology based approach to modeling patient cases and use patients with liver disease conditions as an example. To this end a novel ontology, lico, that utilizes well known medical standards is proposed to represent liver patient cases. The utility of the proposed approach is demonstrated with semantic queries and reasoning using data collected from real patients. The preliminary results are promising in regards to the potentials of ontology based medical case representation for building case-based search and retrieval systems, paving the way towards a Clinical Experience Sharing platform for comparative diagnosis, research, and education.TIN2014-58304 (MINECO) P11-TIC-7529 P12-TIC-1519 TÜBİTAK ARDEB 1001 Programme Grant # 110E264, Boğaziçi University BAP Grant # 5324, COST Action # 1302 (KEYSTONE), Turkish Ministry of Development under the TAM Project number DPT2007K120610

Structured reporting in cardiovascular computed tomography

While investigation techniques and image modalities become more and more advanced, radiology reports have remained in their classic form for the past decades. Structured reporting has shown its potential to increase the clarity, correctness, confidence, concision, completeness, consistency, communication, consultation and standardization of radiology reports. The increased report quality can mostly be attributed to a complete checklist like approach, standardized vocabulary through RadLex and RSNA provided templates which can be adapted to address very specific inquiries. Especially the interdisciplinary approach necessary to design and adapt those templates can ensure that all therapy influencing criteria are evaluated in the report. This may lead to a different therapy and outcome. Structured reporting also harbors great teaching opportunities, such as a checklist-like approach for young radiology residents and an image database of pathological findings. With a large analyzable database of reports, a statistical analysis becomes possible, which can e.g. lead to increasingly better screening algorithms. Technological challenges however, different data formats, varying degrees of quality of structured reporting systems and the concerns about work flow efficiency and report rigidity remain difficulties of structured reporting itself. Despite of this it also provides many future possibilities such as the implementation of medical guide lines into the report format, multi media reports, evaluation of radiation dose, management of follow-up appointments, automatic invoice and reimbursement systems and the improvement of data mining. Given the potential of structured reporting and its impact on patient care, we decided to evaluate its so far unknown benefit for patients with acute PE and PAD. For patients with APE, the structured reports were evaluated by two pulmonologists and two general internists and compared to the reports from the clinical routine of the same patient group. While all four referring clinicians perceived the structured CTPA reports as superior in clarity, only the pulmonologists found additional benefit in content and clinical utility. The structured reports did not alter patients’ management in patients with acute PE significantly. In the study concerning patients with diagnosed or suspected PAD the structured reports (run-off CTA/ lower extremities) were evaluated by two vascular surgeons and two vascular medicine specialists. The results showed, both groups regarded structured reports as superior in clarity, completeness, clinical relevance and usefulness. Especially vascular medicine specialists seemed to appreciate the structured reporting format. As in our PE study, structured reporting did not seem to alter further testing or therapy for the patients included in our study. Both studies demonstrate that referring clinicians prefer structured reporting of cardiovascular CT examinations over conventional reports

Towards semantic interpretation of clinical narratives with ontology-based text mining

In the realm of knee pathology, magnetic resonance imaging (MRI) has the advantage of visualising all structures within the knee joint, which makes it a valuable tool for increasing diagnostic accuracy and planning surgical treatments. Therefore, clinical narratives found in MRI reports convey valuable diagnostic information. A range of studies have proven the feasibility of natural language processing for information extraction from clinical narratives. However, no study focused specifically on MRI reports in relation to knee pathology, possibly due to the complexity of knee anatomy and a wide range of conditions that may be associated with different anatomical entities. In this thesis, we describe KneeTex, an information extraction system that operates in this domain. As an ontology-driven information extraction system, KneeTex makes active use of an ontology to strongly guide and constrain text analysis. We used automatic term recognition to facilitate the development of a domain-specific ontology with sufficient detail and coverage for text mining applications. In combination with the ontology, high regularity of the sublanguage used in knee MRI reports allowed us to model its processing by a set of sophisticated lexico-semantic rules with minimal syntactic analysis. The main processing steps involve named entity recognition combined with coordination, enumeration, ambiguity and co-reference resolution, followed by text segmentation. Ontology-based semantic typing is then used to drive the template filling process. We adopted an existing ontology, TRAK (Taxonomy for RehAbilitation of Knee conditions), for use within KneeTex. The original TRAK ontology expanded from 1,292 concepts, 1,720 synonyms and 518 relationship instances to 1,621 concepts, 2,550 synonyms and 560 relationship instances. This provided KneeTex with a very fine-grained lexicosemantic knowledge base, which is highly attuned to the given sublanguage. Information extraction results were evaluated on a test set of 100 MRI reports. A gold standard consisted of 1,259 filled template records with the following slots: finding, finding qualifier, negation, certainty, anatomy and anatomy qualifier. KneeTex extracted information with precision of 98.00%, recall of 97.63% and F-measure of 97.81%, the values of which are in line with human-like performance. To demonstrate the utility of formally structuring clinical narratives and possible applications in epidemiology, we describe an implementation of KneeBase, a web-based information retrieval system that supports complex searches over the results obtained via KneeTex. It is the structured nature of extracted information that allows queries that encode not only search terms, but also relationships between them (e.g. between clinical findings and anatomical locations). This is of particular value for large-scale epidemiology studies based on qualitative evidence, whose main bottleneck involves manual inspection of many text documents. The two systems presented in this dissertation, KneeTex and KneeBase, operate in a specific domain, but illustrate generic principles for rapid development of clinical text mining systems. The key enabler of such systems is the existence of an appropriate ontology. To tackle this issue, we proposed a strategy for ontology expansion, which proved effective in fast–tracking the development of our information extraction and retrieval systems