Improving diagnostic accuracy in preoperative glioma classification: performance of knowledge-enhanced large language models compared with radiologists.

ABSTRACT: Accurate preoperative MRI classification of gliomas is essential but challenging due to complex radiological features and inter-observer variability. This study evaluated three large language models (LLMs) for VASARI-based glioma classification compared to radiologist interpretations. We retrospectively analyzed 150 histopathologically confirmed gliomas (43 circumscribed astrocytic, 53 high-grade diffuse, 54 low-grade diffuse gliomas) using standardized MRI protocols. Three radiologists extracted VASARI features, while three LLMs (GPT-4, Claude3.5-Sonnet, Claude3.0-Opus) analyzed these features using standard input-output or knowledge-enhanced prompting incorporating diagnostic guidelines. Knowledge-enhanced prompting consistently outperformed standard prompting, improving diagnostic consi

SUBMITTER: Li S 

PROVIDER: S-EPMC12660956 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature