计算成像遇上大语言模型：脑胶质瘤IDH突变状态的零样本预测
Computational Imaging Meets LLMs: Zero-Shot IDH Mutation Prediction in Brain Gliomas
AI总结：
本文提出将大语言模型与计算图像分析相结合的无创零样本框架，用于脑胶质瘤IDH突变预测，在六个公开数据集上验证了其高准确率与均衡性能。
摘要：
我们提出一个框架，将大语言模型（Large Language Models）与计算图像分析相结合，用于脑胶质瘤IDH突变状态的无创、零样本预测。对于每位受试者，对配准的多参数MRI扫描和多类别肿瘤分割图进行处理，提取可解释的语义（视觉）属性与定量特征，将其序列化为标准化JSON文件，并在不进行微调的情况下用于查询GPT 4o和GPT 5。我们在六个公开数据集（N = 1427）上评估了该框架，结果显示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，并辅以亚型特异性影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用于精确、无创的肿瘤基因分型的潜力，推动了神经肿瘤学诊断策略的发展。代码可在https://github.com/ATPLab-LUMS/CIM-LLM获取。
英文摘要：
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.
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。
