A unified representation of findings in clinical radiology using the UMLS and DICOM

https://doi.org/10.1016/j.ijmedinf.2007.11.003Get rights and content

Abstract

Purpose

Collecting and analyzing findings constitute the basis of medical activity. Computer assisted medical activity raises the problem of modelling findings. We propose a unified representation of findings integrating the representations of findings in the GAMUTS in Radiology [M.M. Reeder, B. Felson, GAMUTS in radiology Comprehensive lists of roentgen differential diagnosis, fourth ed., 2003], the Unified Medical Language System (UMLS®), and the Digital Imaging and Communication in Medicine Structured Report (DICOM-SR).

Materials and Methods

Starting from a corpus of findings in bone and joint radiology [M.M. Reeder, B. Felson, GAMUTS in Radiology comprehensive lists of roentgen differential diagnosis, fourth ed., 2003] (3481 words), an automated mapping to the UMLS was performed with the Metamap Program. The resulting UMLS terms and Semantic Types were analyzed in order to find a generic template in accordance with DICOM-SR structure.

Results

UMLS Concepts were missing for 45% of the GAMUTS findings. Three kinds of regularities were observed in the way the Semantic Types were combined: “pathological findings”, “physiological findings” and “anatomical findings”. A generic and original DICOM-SR template modelling finding was proposed. It was evaluated for representing GAMUTS jaws findings. 21% missing terms had to be picked up from Radlex (5%) or created (16%).

Discussion-Conclusion

This article shows that it is possible to represent findings using the UMLS and the DICOM SR formalism with a semi-automated method. The Metamap program helped to find a model to represent the semantic structure of free texts with standardized terms (UMLS Concepts). Nevertheless, the coverage of the UMLS is not comprehensive. This study shows that the UMLS should include more technical concepts and more concepts regarding findings, signs and symptoms to be suitable for radiology representation. The semi-automated translation of the whole GAMUTS using the UMLS concepts and the DICOM SR relations could help to create or supplement the DCMR Templates and Context Groups pertaining to the description of imaging findings.

Introduction

Computer assisted medical activity raises the problem of modelling medical information. Indeed structured modelling using a controlled vocabulary allows the information to be standardized and presented in a clear, organized format. In addition to improving readability, structured modelling allows improved information retrieval and automated analysis for decision-support, research (data mining), evidence based medicine and teaching. Collecting and analyzing findings constitute the basis of medical activity. Creating and standardizing a structured radiology report is already the goal of the work in progress with the DICOM Structured Report (SR). But no unified model of findings does still exist.

The aim of this work is to propose a unified, sharable structured representation of findings integrating the representations of findings in the GAMUTS in Radiology from Reeder and Felson [1], the UMLS®, and the DICOM-SR. Starting from a corpus of findings in natural language, we used a semi-automated method in order to find a generic model (or template) for findings. Our model was evaluated for jaws radiological findings.

Section snippets

The GAMUTS in Radiology

A corpus has to gather information for which the to-build structured representation will have to be used: here typically textbooks or clinical reports. We started from descriptions of radiological findings which are proposed in many books [1], [2], [3]. Most of them have been edited many times and some have been translated in several languages [4]. However, the descriptions of findings remain unchanged (in vocabulary and in structure) in various books from various authors and in various

Building the corpus of findings

The finding corpus contained 504 different phrases, 3481 words, 931 different words.

Mapping the corpus of findings in natural language to the UMLS

Metamap discovered 5046 concepts (1220 different CUIs). After manual cleaning, 1888 concepts (37%) were kept (583 different CUI, i.e. 48%).

Discussion

In this work, we showed that it is possible to represent findings using the UMLS and the DICOM SR formalism with a semi-automated method. The UMLS and the Metamap program helped to find a model to represent the semantic structure of free texts with standardized terms. Metamapping the GAMUTS is a semi automated method to create new Templates and new Context Groups thanks to UMLS Concepts and Semantic Types.

Mapping free text to Metathesaurus concepts allows reusing the semantic structure of the

Conclusion

This study shows that the UMLS should include more technical and concepts pertaining to findings in radiology to be suitable for radiology representation. It also suggests that DICOM could build its new Templates and Context Groups respecting, if possible, UMLS Semantic Types.

The translation of the whole GAMUTS or clinical reports using the UMLS concepts and the DICOM SR relations could help to create or supplement the DCMR Templates and Context Groups in the field of the description of imaging

References (12)

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    GAMUTS in Radiology Comprehensive lists of roentgen differential diagnosis

    (2003)
  • R.L. Eisenberg

    Clinical Imaging. An Atlas of Differential Diagnosis

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    Guide du diagnostic différentiel en radiologie

    Vigot

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  • M.M. Reeder et al.

    GAMUTS in Radiology Comprehensive Lists of Rroentgen Differential Diagnosis

    (1975)
  • UMLS Knowledge Source Server Version 4.2.3....
There are more references available in the full text version of this article.

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