用大模型分析MRI影像，零样本预测脑胶质瘤IDH突变状态。
Computational Imaging Meets LLMs: Zero-Shot IDH Mutation Prediction in Brain Gliomas
- 融合多模态MRI与分割图，生成标准化特征序列输入大模型
- 在1427例数据上实现高精度分类，无需标注与微调
- 适合临床神经肿瘤学、医学影像分析人员快速应用
我们提出一种将大语言模型与计算图像分析结合的框架，用于非侵入性、零样本预测脑胶质瘤的IDH突变状态。每位患者的核心配准多参数MRI扫描及多类别肿瘤分割图被处理，提取可解释的语义（视觉）属性与定量特征，序列化为标准JSON文件，直接用于GPT 4o和GPT 5的无微调查询。该框架在六个公开数据集（N = 1427）上评估，展现了跨异质队列的高准确率与均衡分类性能，即使在无手动标注条件下依然有效。GPT 5在上下文驱动的表型解读中表现优于GPT 4o。体积特征成为最重要的预测因子，辅以亚型特异性影像标志物与临床信息。结果表明，结合大模型推理与计算图像分析，可实现精准、非侵入性肿瘤基因分型，推动神经肿瘤诊断策略发展。代码已开源：https://github.com/ATPLab-LUMS/CIM-LLM。
原文摘要 · Abstract (English)
We present a framework that combines Large Language Models with computational image analytics for non-invasive, zero-shot prediction of IDH mutation status in brain gliomas. For each subject, coregistered multi-parametric MRI scans and multi-class tumor segmentation maps were processed to extract interpretable semantic (visual) attributes and quantitative features, serialized in a standardized JSON file, and used to query GPT 4o and GPT 5 without fine-tuning. We evaluated this framework on six publicly available datasets (N = 1427) and results showcased high accuracy and balanced classification performance across heterogeneous cohorts, even in the absence of manual annotations. GPT 5 outperformed GPT 4o in context-driven phenotype interpretation. Volumetric features emerged as the most important predictors, supplemented by subtype-specific imaging markers and clinical information. Our results demonstrate the potential of integrating LLM-based reasoning with computational image analytics for precise, non-invasive tumor genotyping, advancing diagnostic strategies in neuro-oncology. The code is available at https://github.com/ATPLab-LUMS/CIM-LLM.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。