ArticleNPJ precision oncology2025
Improving diagnostic accuracy in preoperative glioma classification: performance of knowledge-enhanced large language models compared with radiologists.
Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Classification of histology and molecular subtypes of brain gliomas and glioneuronal and neuronal tumors using a deep learning approach.European radiology · 2026Article
- [Digital transformation in endocrinology/diabetology, pneumology, and hematology/oncology : Where do we stand today-and what lies ahead?]Innere Medizin (Heidelberg, Germany) · 2026Review
- Navigating the complexity of WHO CNS5: the evolutionary trajectory of glioma classification and the emergence of large language models.Frontiers in oncology · 2026Review
- ASLNet: an explainable deep learning framework for glioma grading and survival prediction.Frontiers in oncology · 2026Article
Corrections and comments
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Authors and funding
10 authors.
Funding
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 consistency (intra-model agreement: Sonnet κ = 0.91, Opus κ = 0.92, GPT-4 κ = 0.72). For diffuse versus circumscribed classification, senior radiologists (AUC = 0.88) and Claude3.5-Sonnet with knowledge-enhanced prompting (AUC = 0.84) performed similarly (p > 0.05). LLM assistance significantly improved junior radiologists' performance, with AUC increases from 0.77 to 0.83 (p = 0.026). Knowledge-enhanced LLMs demonstrate diagnostic performance comparable to experienced radiologists and improve junior accuracy, suggesting potential as decision-support tools requiring radiologist oversight.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.