Navigating the complexity of WHO CNS5: the evolutionary trajectory of glioma classification and the emergence of large language models

Engineering notes

While this evolution has significantly enhanced diagnostic precision, it has also imposed a substantial cognitive burden on clinicians.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

The 2021 World Health Organization (WHO) Classification of Tumors of the Central Nervous System (fifth edition, WHO CNS5) marks a profound paradigm shift from traditional morphologic assessment to a biologically and molecularly integrated diagnostic framework. While this evolution has significantly enhanced diagnostic precision, it has also imposed a substantial cognitive burden on clinicians. This challenge is particularly evident when navigating the fragmented landscape of molecular biomarkers and the increasingly intricate grading logic required for precise classification. This review delineates the historical trajectory of glioma classification standards and evaluates the recent research progress of Large Language Models (LLMs) in assisting neuro-oncological text processing. Evidence suggests that through advanced technologies such as Retrieval-Augmented Generation (RAG) and Reinforcement Learning from Human Feedback (RLHF), LLMs can effectively synthesize unstructured clinical data, mitigate the risk of “hallucinations,” and generate integrated diagnostic recommendations compliant with the latest standards. The transition toward an intelligent diagnostic paradigm is poised to provide critical support for the precise classification and personalized therapeutic closure of gliomas.

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