Structured radiology reporting is advantageous for optimizing clinical workflows and patient outcomes. Current LLMs in creating structured reports face the challenges of formatting errors, content hallucinations, and privacy leakage concerns when uploaded to external servers. We aim to develop an enhanced open-source LLM for creating structured and standardized LCS reports from free-text descriptions. After institutional IRB approvals, 5,442 de-identified LCS reports from two institutions were retrospectively analyzed. 500 reports were randomly selected from the two institutions evenly and then manually labeled for evaluation. Two radiologists from the two institutions developed a standardized template including 29 features for lung nodule reporting. We proposed template-constrained decoding to enhance state-of-the-art open-source LLMs, including LLAMA, Qwen, and Mistral. The LLM performance was extensively evaluated in terms of F1 score, confidence interval, McNemar test, and z-test. Based on the structured reports created from the large-scale dataset, a nodule-level retrieval system was prototyped and an automatic statistical analysis was performed. Our software, vLLM-structure, is publicly available for local deployment with enhanced LLMs. Our template-constrained decoding approach consistently enhanced the LLM performance on multi-institutional datasets, with neither formatting errors nor content hallucinations. Our method improved the best open-source LLAMA-3.1 405B by up to 10.42%, and outperformed GPT-4o by 17.19%. A novel nodule retrieval system was successfully prototyped and demonstrated on a large-scale multimodal database using our enhanced LLM technologies. The automatically derived statistical distributions were closely consistent with the prior findings in terms of nodule type, location, size, status, and Lung-RADS.

本研究针对当前大型语言模型在创建结构化放射学报告时面临的格式错误和内容幻觉等挑战，提出了一种基于模板约束解码的增强型开源大型语言模型。研究发现，该方法在多机构数据集上显著提升了评估性能，并成功展示了一个新型肺结节检索系统的原型，为临床工作流优化和患者治疗结果改善提供了新的可能性。

跨机构结构化放射学报告在肺癌筛查中的应用：基于动态模板约束的大型语言模型