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.

翻译：我们提出了一种将大型语言模型与计算图像分析相结合的框架，用于脑胶质瘤IDH突变状态的非侵入性零样本预测。针对每个受试者，我们处理了配准后的多参数MRI扫描和多类别肿瘤分割图，以提取可解释的语义（视觉）属性和定量特征，将其序列化为标准化的JSON文件，并用于查询GPT 4o和GPT 5模型，无需微调。我们在六个公开数据集（N = 1427）上评估了该框架，结果显示其在异质性队列中具有高准确率和均衡的分类性能，即使在没有人工标注的情况下也是如此。GPT 5在上下文驱动的表型解释方面优于GPT 4o。体积特征成为最重要的预测因子，辅以亚型特异性成像标志物和临床信息。我们的结果证明了将基于LLM的推理与计算图像分析相结合，可实现精确、非侵入性的肿瘤基因分型，推动了神经肿瘤学诊断策略的进步。代码可在 https://github.com/ATPLab-LUMS/CIM-LLM 获取。