使用大型语言模型自动生成PET报告个性化印象
Automatic Personalized Impression Generation for PET Reports Using Large Language Models
AI总结：
本研究通过微调PEGASUS等大型语言模型，结合医生身份编码，自动生成个性化PET报告印象，实验表明其临床可接受率达89%，效用与人工印象相当，可加快报告流程。
摘要：
本研究旨在确定经过微调的大型语言模型（LLMs）能否为全身PET报告生成准确、个性化的印象。使用教师强制（teacher-forcing）算法，在PET报告语料库上训练了十二个语言模型，以报告发现（findings）为输入，以临床印象（impressions）为参考。一个额外的输入标记编码阅片医生的身份，使模型能够学习医生特定的报告风格。我们的语料库包含2010年至2022年间从本机构收集的37,370份回顾性PET报告。为确定最佳LLM，将30项评估指标与两位核医学（NM）医生的质量评分进行基准比较，选择与医生偏好最一致的指标用于专家评估。在一个数据子集中，三位NM医生根据6个质量维度（3分制）和总体效用评分（5分制）对模型生成的印象和原始临床印象进行评估。每位医生审阅了12份自己的报告和12份其他医生的报告。使用自助重采样（bootstrap resampling）进行统计分析。在所有评估指标中，领域自适应的BARTScore和PEGASUSScore与医生偏好的Spearman秩相关性最高（分别为0.568和0.563）。基于这些指标，微调后的PEGASUS模型被选为最佳LLM。当医生审阅PEGASUS以自身风格生成的印象时，89%被认为临床可接受，平均效用评分为4.08分（满分5分）。医生认为这些个性化印象的总体效用与其他医生口述的印象相当（4.03，P=0.41）。总之，PEGASUS生成的个性化印象具有临床实用性，凸显了其加快PET报告生成的潜力。
英文摘要：
In this study, we aimed to determine if fine-tuned large language models (LLMs) can generate accurate, personalized impressions for whole-body PET reports. Twelve language models were trained on a corpus of PET reports using the teacher-forcing algorithm, with the report findings as input and the clinical impressions as reference. An extra input token encodes the reading physician's identity, allowing models to learn physician-specific reporting styles. Our corpus comprised 37,370 retrospective PET reports collected from our institution between 2010 and 2022. To identify the best LLM, 30 evaluation metrics were benchmarked against quality scores from two nuclear medicine (NM) physicians, with the most aligned metrics selecting the model for expert evaluation. In a subset of data, model-generated impressions and original clinical impressions were assessed by three NM physicians according to 6 quality dimensions (3-point scale) and an overall utility score (5-point scale). Each physician reviewed 12 of their own reports and 12 reports from other physicians. Bootstrap resampling was used for statistical analysis. Of all evaluation metrics, domain-adapted BARTScore and PEGASUSScore showed the highest Spearman's rank correlations (0.568 and 0.563) with physician preferences. Based on these metrics, the fine-tuned PEGASUS model was selected as the top LLM. When physicians reviewed PEGASUS-generated impressions in their own style, 89% were considered clinically acceptable, with a mean utility score of 4.08 out of 5. Physicians rated these personalized impressions as comparable in overall utility to the impressions dictated by other physicians (4.03, P=0.41). In conclusion, personalized impressions generated by PEGASUS were clinically useful, highlighting its potential to expedite PET reporting.
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