Semantic representation of reported measurements in radiology.

Heiner Oberkampf,James A Overton,Michael Uder,Bernhard Bauer,Matthias Hammon,Alexander Cavallaro,Sonja Zillner

doi:10.1186/s12911-016-0248-9

Abstract

BackgroundIn radiology, a vast amount of diverse data is generated, and unstructured reporting is standard. Hence, much useful information is trapped in free-text form, and often lost in translation and transmission. One relevant source of free-text data consists of reports covering the assessment of changes in tumor burden, which are needed for the evaluation of cancer treatment success. Any change of lesion size is a critical factor in follow-up examinations. It is difficult to retrieve specific information from unstructured reports and to compare them over time. Therefore, a prototype was implemented that demonstrates the structured representation of findings, allowing selective review in consecutive examinations and thus more efficient comparison over time.MethodsWe developed a semantic Model for Clinical Information (MCI) based on existing ontologies from the Open Biological and Biomedical Ontologies (OBO) library. MCI is used for the integrated representation of measured image findings and medical knowledge about the normal size of anatomical entities. An integrated view of the radiology findings is realized by a prototype implementation of a ReportViewer. Further, RECIST (Response Evaluation Criteria In Solid Tumors) guidelines are implemented by SPARQL queries on MCI. The evaluation is based on two data sets of German radiology reports: An oncologic data set consisting of 2584 reports on 377 lymphoma patients and a mixed data set consisting of 6007 reports on diverse medical and surgical patients. All measurement findings were automatically classified as abnormal/normal using formalized medical background knowledge, i.e., knowledge that has been encoded into an ontology. A radiologist evaluated 813 classifications as correct or incorrect. All unclassified findings were evaluated as incorrect.ResultsThe proposed approach allows the automatic classification of findings with an accuracy of 96.4 % for oncologic reports and 92.9 % for mixed reports. The ReportViewer permits efficient comparison of measured findings from consecutive examinations. The implementation of RECIST guidelines with SPARQL enhances the quality of the selection and comparison of target lesions as well as the corresponding treatment response evaluation.ConclusionsThe developed MCI enables an accurate integrated representation of reported measurements and medical knowledge. Thus, measurements can be automatically classified and integrated in different decision processes. The structured representation is suitable for improved integration of clinical findings during decision-making. The proposed ReportViewer provides a longitudinal overview of the measurements.

Highlights

In radiology, a vast amount of diverse data is generated, and unstructured reporting is standard
1 We describe classes and properties of Model for Clinical Information (MCI) that are used for the structured representation of size measurements typically found in radiology reports and medical knowledge about the normal size of anatomical entities
Normality classification of image findings In [12], we demonstrated how structured representations of measurement findings can be extracted from free-text radiology reports by using Natural Language Processing technology in combination with the knowledge models described above

Summary

Introduction

A vast amount of diverse data is generated, and unstructured reporting is standard. One relevant source of free-text data consists of reports covering the assessment of changes in tumor burden, which are needed for the evaluation of cancer treatment success. Any change of lesion size is a critical factor in follow-up examinations. Because unstructured reporting is the norm, much useful information is trapped in free-text form, and often lost in translation and transmission [1, 2]. Many different measurements are performed and reported in radiology (volumes, perfusion or diffusion measurements, spectroscopy results, etc.) Most of these data are measurements of the size of a lesion or an organ in different dimensions. The change of the lesion size is still the critical factor in follow-up examinations (sonography, computed tomography, magnetic resonance imaging) during therapy or surveillance. A sum of the diameters (short axis for lymph nodes, longest diameter for the remaining lesions) for all target lesions is calculated and reported as the baseline or follow-up sum and indicates therapy response/failure [3]

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Conclusion