American Journal of Roentgenology最新文献

Background: RadLex is a widely used radiology-specific ontology that standardizes terminology for clinical and research uses. However, the ontology's coverage of clinical radiology reports remains limited due to radiologists' linguistic variation. Objective: To use a large language model (LLM) to generate an expanded set of lexical variants and synonyms for the RadLex ontology and to evaluate the impact of this expansion on lexical coverage and semantic term recognition using clinical radiology reports. Methods: This retrospective study used an LLM (Gemini 2.0 Flash Thinking) to generate an expansion (lexical variants [morphologic variants, orthographic variants, and acronyms and abbreviations] and strict semantic synonyms) of the 40,000 RadLex preferred terms, with detailed constraints to ensure semantic alignment. Five datasets of clinical chest CT reports were obtained (two from the study institution in Korea [n=119,098 and n=245], three from public datasets [Spain, n=5213; Turkey, n=21,304; United States, n=19,405]). The same LLM was used to parse the reports into lexicon units (concise text strings representing distinct medical concepts). For each dataset, the lexical coverage rate was automatically computed as a measure of the extent to which the parsed units matched a given expression list. Additionally, 100 randomly selected reports from each dataset were manually reviewed to determine a given expression list's precision, recall, and F1 score (measures of unit-level matching performance when requiring semantic fidelity). Metrics were compared between the existing RadLex-provided expansion and the LLM-generated expansion. Results: The RadLex-provided expansion contained 17,515 terms. The LLM-generated expansion contained 208,465 lexical variants and 69,918 synonyms. For all five datasets, the LLM-generated expansion, compared with the RadLex-provided expansion, had a greater lexical coverage rate (81.9-85.6% vs 67.5-75.3%), greater recall (81.6-91.4% vs 64.0-80.3%), lower precision (94.8-98.2% vs 100.0% [all datasets]), and greater F1 score (0.91-0.95 vs 0.86-0.91). Conclusion: Across multinational datasets of clinical chest CT reports, the LLM-generated term expansion yielded improved lexical coverage and semantic recall, with only small loss of semantic precision, compared with the RadLex-provided expansion. Clinical Impact: The LLM-based approach provides a practical and scalable solution for expanding radiology ontologies while maintaining semantic alignment; the method can aid real-world natural language processing applications.

Crohn disease is a chronic immune-mediated gastrointestinal disorder affecting nearly one million people in the United States. Despite therapeutic advances, many patients do not achieve durable intestinal healing, and stricture-related complications, including internal penetrating disease, remain frequent. Disease often progresses radiologically from isolated active inflammation (characterized by bowel wall thickening), to associated luminal narrowing (i.e., >50% diameter reduction), to associated upstream dilatation (i.e., overt stricture, with obstruction risk). The intermediate stage of wall thickening with luminal narrowing is associated with distinct biomarker profiles, microbial signatures, gene expression patterns, imaging features, and prognostic outcomes. Accordingly, consensus statements from the Society of Abdominal Radiology Inflammatory Bowel Disease Disease-Focus Panel indicate that patients with bowel wall thickening and fixed luminal narrowing without upstream dilation should be considered to have probable strictures. Yet, current clinical classification systems do not fully incorporate this radiologic entity. For example, the Montreal and Paris classifications have historically classified patients with wall thickening and luminal narrowing as having the inflammatory rather than stricturing phenotype. This Perspective summarizes evidence supporting probable strictures as a distinct biologic and clinical entity that could aid individualized care and argues for integrating probable stricturing into radiology reports, clinical classification schemes, and treatment pathways.