用时间轴形式自动总结肺部影像报告，便于追踪病灶变化。
RadTimeline: Timeline Summarization for Longitudinal Radiological Lung Findings
- 将纵向报告转化为带时间戳的结构化时间轴，按时间分列、相关发现分组。
- 最佳模型召回率高，分组效果接近人工标注，但存在少量无关发现。
- 适合需要长期追踪肺部疾病进展的临床医生和研究者使用。
追踪纵向放射科报告中的发现对准确识别疾病进展至关重要，而手动处理耗时费力，自动化总结可显著提升效率。本文提出一种结构化摘要任务，将纵向报告摘要转化为时间轴生成任务：将带日期的发现按时间列排列，将时间相关的发现归为同一行。该格式便于跨时间比较发现，并支持与原始报告的事实核对。时间轴通过三步大模型流程生成：提取发现、生成分组名称、用名称对发现进行分组。为评估该方法，我们构建了专注于胸部影像中肺部发现追踪的RadTimeline数据集。在该数据集上的实验揭示不同规模大模型及提示策略的权衡。结果表明，中间步骤生成分组名称对有效分组至关重要。最优配置虽含少量无关发现，但召回率高，分组性能接近人类标注者。
原文摘要 · Abstract (English)
Tracking findings in longitudinal radiology reports is crucial for accurately identifying disease progression, and the time-consuming process would benefit from automatic summarization. This work introduces a structured summarization task, where we frame longitudinal report summarization as a timeline generation task, with dated findings organized in columns and temporally related findings grouped in rows. This structured summarization format enables straightforward comparison of findings across time and facilitates fact-checking against the associated reports. The timeline is generated using a 3-step LLM process of extracting findings, generating group names, and using the names to group the findings. To evaluate such systems, we create RadTimeline, a timeline dataset focused on tracking lung-related radiologic findings in chest-related imaging reports. Experiments on RadTimeline show tradeoffs of different-sized LLMs and prompting strategies. Our results highlight that group name generation as an intermediate step is critical for effective finding grouping. The best configuration has some irrelevant findings but very good recall, and grouping performance is comparable to human annotators.
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