GPT-5在脑肿瘤MRI推理中的表现
Performance of GPT-5 in Brain Tumor MRI Reasoning
AI总结：
本研究在源自三个BraTS数据集的脑肿瘤视觉问答基准上评估了GPT-4o及GPT-5系列模型的零样本推理能力，发现其准确率仅达中等水平，尚不能满足临床使用要求。
摘要：
在神经肿瘤学中，通过磁共振成像（MRI）准确区分脑肿瘤类型对于指导治疗规划至关重要。大型语言模型（LLMs）的最新进展催生了视觉问答（VQA）方法，该方法将图像解释与自然语言推理相结合。在本研究中，我们在一个精选的脑肿瘤VQA基准上评估了GPT-4o、GPT-5-nano、GPT-5-mini和GPT-5，该基准源自3个Brain Tumor Segmentation（BraTS）数据集——胶质母细胞瘤（GLI）、脑膜瘤（MEN）和脑转移瘤（MET）。每个病例均包含多序列MRI三平面马赛克图像以及转化为标准化VQA条目的结构化临床特征。在零样本思维链设置下，对模型在视觉和推理任务上的准确性进行了评估。结果显示，GPT-5-mini取得了最高的宏平均准确率（44.19%），其次是GPT-5（43.71%）、GPT-4o（41.49%）和GPT-5-nano（35.85%）。性能因肿瘤亚型而异，没有单一模型在所有队列中均占主导地位。这些发现表明，GPT-5系列模型在结构化神经肿瘤学VQA任务中能够达到中等准确率，但尚未达到临床可接受的水平。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
