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

Vertebral compression fractures in multiple myeloma : Part II. Assessment of fracture risk with MR imaging of spinal bone marrow

PURPOSE: To determine the utility of bone marrow magnetic resonance (MR) imaging in the assessment of risk of vertebral compression fractures in patients with multiple myeloma. MATERIALS AND METHODS: In 50 patients with stage III multiple myeloma, 280 MR examinations of the thoracolumbar spine obtained at diagnosis and during treatment (mean follow-up, 28 months) were analyzed to determine MR patterns of bone marrow involvement before treatment and the occurrence of vertebral compression fracture at follow-up. Four MR patterns of marrow involvement were determined: A, normal marrow appearance; B, fewer than 10 focal lesions; C, more than 10 focal lesions; and D, diffuse infiltration. Fracture-free survival was compared according to these patterns. RESULTS: During follow-up, 131 vertebral compression fractures appeared in 37 patients. Patients with pattern A (n = 10) or B (n = 16) had significantly longer fracture-free survival before occurrence of the first, second, and third fractures than those with pattern C or D (P < 10(-5)). Relative risks of first, second, and third fracture occurrence for patients with pattern C or D compared with those with pattern A or B were 6.2, 9.1, and 11.0, respectively. CONCLUSION: Determination of MR patterns of spinal bone marrow involvement is a potential relevant factor to predict the risk of vertebral fractures in patients with stage III multiple myeloma