RadTimeline: Timeline Summarization for Longitudinal Radiological Lung Findings

Abstract

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.

Figures (2)

• Figure 1: The timeline task and a three-step LLM approach. Each column corresponds to a time-stamped radiology exam (e.g., YYYY-05_chest-ct). Each cell in a column is a piece of lung finding fact (e.g., "stable subsolid pulmonary nodule in the right upper lobe") from that report including clinically important details (e.g., "stable", "subsolid"). Each row groups temporally related findings, with a row header describing the distinguishing characteristics of that group (e.g., "right upper lobe ground glass nodule").
• Figure 2: A group assignment example provided to annotators.