GPT-5系列模型在脑肿瘤MRI问答中表现中等，尚不适用于临床。
Performance of GPT-5 in Brain Tumor MRI Reasoning
- 用多序列MRI图像和临床信息构建脑肿瘤VQA评测集，测试大模型推理能力。
- GPT-5-mini准确率最高达44.19%，但所有模型均未达到临床可用水平。
- 模型表现因肿瘤类型而异，无单一模型全面领先，适合研究辅助决策系统。
在神经肿瘤学中，准确区分脑肿瘤类型对治疗方案制定至关重要。近年来，大型语言模型（LLMs）的发展推动了视觉问答（VQA）方法，将图像解读与自然语言推理相结合。本研究评估了GPT-4o、GPT-5-nano、GPT-5-mini和GPT-5在基于3个脑肿瘤分割（BraTS）数据集（胶质母细胞瘤GLI、脑膜瘤MEN、脑转移瘤MET）构建的脑肿瘤VQA基准上的表现。每个病例包含多序列MRI三平面拼接图及标准化临床特征转化的结构化VQA问题。模型在零样本链式思维设置下评估视觉与推理任务的准确性。结果显示，GPT-5-mini达到最高宏平均准确率（44.19%），其次为GPT-5（43.71%）、GPT-4o（41.49%）、GPT-5-nano（35.85%）。不同肿瘤亚型间性能差异明显，无模型在所有队列中占优。结果表明，GPT-5系列模型可在结构化神经肿瘤VQA任务中实现中等准确率，但尚未达到临床应用标准。
原文摘要 · Abstract (English)
Accurate differentiation of brain tumor types on magnetic resonance imaging (MRI) is critical for guiding treatment planning in neuro-oncology. Recent advances in large language models (LLMs) have enabled visual question answering (VQA) approaches that integrate image interpretation with natural language reasoning. In this study, we evaluated GPT-4o, GPT-5-nano, GPT-5-mini, and GPT-5 on a curated brain tumor VQA benchmark derived from 3 Brain Tumor Segmentation (BraTS) datasets - glioblastoma (GLI), meningioma (MEN), and brain metastases (MET). Each case included multi-sequence MRI triplanar mosaics and structured clinical features transformed into standardized VQA items. Models were assessed in a zero-shot chain-of-thought setting for accuracy on both visual and reasoning tasks. Results showed that GPT-5-mini achieved the highest macro-average accuracy (44.19%), followed by GPT-5 (43.71%), GPT-4o (41.49%), and GPT-5-nano (35.85%). Performance varied by tumor subtype, with no single model dominating across all cohorts. These findings suggest that GPT-5 family models can achieve moderate accuracy in structured neuro-oncological VQA tasks, but not at a level acceptable for clinical use.
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