RadLex Initiative Aims to Standardize Imaging Series Naming Conventions

Inconsistent naming of imaging series can hinder radiology workflows, causing unreliable hanging protocols and difficulties in automating image selection and analysis. To address these issues, RadLex, RSNA's controlled radiology lexicon, is developing standardized imaging series naming conventions aimed at reducing variability across imaging systems, vendors, and institutions.

Series descriptions are often stored as free text, leading to significant variation not only across vendors and institutions but also for identical protocols performed on different scanners. John Mongan, MD, PhD, associate chair for translational informatics at the University of California, San Francisco, highlighted the challenge: "There are a million different ways that the same information could be represented, making it difficult to programmatically identify a particular series."

RadLex has been widely used for standardized radiology exam naming and is now expanding its scope to tackle variability at the imaging series level. The differences in series naming can directly impact clinical workflows. According to Audrey Verde, MD, PhD, neuroradiologist and chair of the RSNA RadLex Committee, "Hanging protocols frequently fail to display images consistently, no matter what vendor PACS you have, because naming conventions differ across systems."

Stacy O’Connor, MD, MPH, MMS, a professor of radiology at the University of Rochester Medical Center, noted that integrating new sites often entails extensive remapping of naming conventions. She emphasized the importance of seamless data handoffs across multiple systems, stating, "The life cycle of a radiology exam depends on seamless data handoffs across multiple systems. When data varies across sites and modalities, those breakdowns can affect the radiologist workflow and ultimately patient care."

As imaging volumes grow and systems evolve, the consequences of inconsistent naming become more pronounced. Greg Zaharchuk, MD, PhD, professor of radiology at Stanford University, indicated that the accumulation of variability across scanners, vendors, and institutions can undermine otherwise well-designed systems, stating, "It undermines otherwise well-designed systems and limits adoption of tools that could improve efficiency and accuracy."

Despite a shared understanding of the need for standardization, reaching consensus on how to define and structure series naming remains complex. Dr. Mongan observed, "There may be hundreds or thousands of equally valid approaches, reflecting differences in personal and institutional preferences."

Standardizing series naming could facilitate more consistent downstream use of imaging data across clinical workflows, research, and AI applications. Dr. O’Connor emphasized that with standardized naming, downstream processes could be automated, improving efficiency and reducing manual effort. Furthermore, standardized naming enables systems to accurately identify relevant prior studies, apply appropriate report templates, and support consistent hanging protocols. Dr. Verde concluded, "While standardization requires upfront investment, the opportunity for efficiency is substantial, supporting better use of data for clinical care, research, and operation."