Researchers streamline pilot radiological lexicon

Article

Using ontologies and data analysis techniques, researchers have helped clean up RadLex, the RSNA's pilot radiological lexicon project, according to two studies presented at the 2005 RSNA meeting.

Using ontologies and data analysis techniques, researchers have helped clean up RadLex, the RSNA's pilot radiological lexicon project, according to two studies presented at the 2005 RSNA meeting.

Maintaining and curating RadLex is difficult, said Dr. Daniel L. Rubin, a clinical assistant professor at Stanford University Medical Center and executive director of the National Center for Biomedical Ontology. RadLex resides in a flat file format that is friendly to computers but unfriendly to people.

Rubin outlined how ontologies, which describe concepts and relationships in a way that is comprehensible to humans and usable by computers, can help identify omissions and redundancies in RadLex.

Using a program developed at Stanford called Protege, Rubin and colleagues mapped RadLex terms and attributes to ontology classes and relationships. This created a graphical, user-friendly way to browse through the terms.

The ontology model allowed researchers to determine that 23 of the 1326 RadLex concepts are synonyms. They also found 17 duplicate terms, with 15 of them in separate ontological subtrees. Because the terms resided in entirely different subtrees, they were probably not redundant, according to Rubin. The other two cases were in the same subtrees and probably did represent redundant terms.

Dr. Dirk Marwede at the University Hospital in Leipzig, Germany, presented the results of his analysis comparing radiological terms found in thoracic CT reports with terms used in RadLex's draft lexicon for thoracic radiology.

Marwede and colleagues randomly selected 250 thoracic CT reports from a database. They then extracted and compared the appropriate terms from the reports with terms found in RadLex. They found 512 relevant terms, 68% of which were found in the RadLex term categories, "findings" and "anatomic location." Marwede reported that the visual features and anatomic location findings were the most frequently used RadLex categories.

The study found that 164 terms did not appear in RadLex. While most terms used in thoracic CT reports are already contained in RadLex, relationships defined in the lexicon need to be more specific, and indexing report content needs well-defined encoding rules, Marwede said.

The complete RadLex lexicon should be available by the 2006 RSNA meeting.

Recent Videos
Improving Access to Nuclear Imaging: An Interview with SNMMI President Jean-Luc C. Urbain, MD, PhD
SNMMI: 18F-Piflufolastat PSMA PET/CT Offers High PPV for Local PCa Recurrence Regardless of PSA Level
SNMMI: NIH Researcher Discusses Potential of 18F-Fluciclovine for Multiple Myeloma Detection
SNMMI: What Tau PET Findings May Reveal About Modifiable Factors for Alzheimer’s Disease
Emerging Insights on the Use of FES PET for Women with Lobular Breast Cancer
Can Generative AI Reinvent Radiology Reporting?: An Interview with Samir Abboud, MD
Mammography Study Reveals Over Sixfold Higher Risk of Advanced Cancer Presentation with Symptom-Detected Cancers
Combining Advances in Computed Tomography Angiography with AI to Enhance Preventive Care
Study: MRI-Based AI Enhances Detection of Seminal Vesicle Invasion in Prostate Cancer
What New Research Reveals About the Impact of AI and DBT Screening: An Interview with Manisha Bahl, MD
Related Content
© 2025 MJH Life Sciences

All rights reserved.