Radiology reports capture crucial longitudinal information on tumor burden, treatment response, and disease progression, yet their unstructured narrative format complicates automated analysis. While large language models (LLMs) have advanced clinical text processing, most state-of-the-art systems remain proprietary, limiting their applicability in privacy-sensitive healthcare environments. We present a fully open-source, locally deployable pipeline for longitudinal information extraction from radiology reports, implemented using the \texttt{llm\_extractinator} framework. The system applies the \texttt{qwen2.5-72b} model to extract and link target, non-target, and new lesion data across time points in accordance with RECIST criteria. Evaluation on 50 Dutch CT Thorax/Abdomen report pairs yielded high extraction performance, with attribute-level accuracies of 93.7\% for target lesions, 94.9\% for non-target lesions, and 94.0\% for new lesions. The approach demonstrates that open-source LLMs can achieve clinically meaningful performance in multi-timepoint oncology tasks while ensuring data privacy and reproducibility. These results highlight the potential of locally deployable LLMs for scalable extraction of structured longitudinal data from routine clinical text.

翻译：放射学报告记录了关于肿瘤负荷、治疗反应和疾病进展的关键纵向信息，但其非结构化的叙述性格式使自动化分析变得复杂。尽管大语言模型（LLMs）在临床文本处理方面取得了进展，但大多数最先进的系统仍是专有的，这限制了它们在注重隐私的医疗环境中的适用性。我们提出了一个完全开源、可本地部署的流程，用于从放射学报告中提取纵向信息，该流程使用 \texttt{llm\_extractinator} 框架实现。该系统应用 \texttt{qwen2.5-72b} 模型，根据 RECIST 标准跨时间点提取并关联靶病灶、非靶病灶和新发病灶的数据。在 50 对荷兰语胸部/腹部 CT 报告上的评估显示出较高的提取性能，属性级准确率分别为：靶病灶 93.7\%，非靶病灶 94.9\%，新发病灶 94.0\%。该方法表明，开源 LLMs 可以在确保数据隐私和可重复性的同时，在多时间点肿瘤学任务中达到具有临床意义的性能。这些结果凸显了可本地部署的 LLMs 在从常规临床文本中可扩展地提取结构化纵向数据方面的潜力。