With the growing use of transformer-based language models in medicine, it is
unclear how well these models generalize to nuclear medicine which has
domain-specific vocabulary and unique reporting styles. In this study, we
evaluated the value of domain adaptation in nuclear medicine by adapting
language models for the purpose of 5-point Deauville score prediction based on
clinical 18F-fluorodeoxyglucose (FDG) PET/CT reports. We retrospectively
retrieved 4542 text reports and 1664 images for FDG PET/CT lymphoma exams from
2008-2018 in our clinical imaging database. Deauville scores were removed from
the reports and then the remaining text in the reports was used as the model
input. Multiple general-purpose transformer language models were used to
classify the reports into Deauville scores 1-5. We then adapted the models to
the nuclear medicine domain using masked language modeling and assessed its
impact on classification performance. The language models were compared against
vision models, a multimodal vision language model, and a nuclear medicine
physician with seven-fold Monte Carlo cross validation, reported are the mean
and standard deviations. Domain adaption improved all language models. For
example, BERT improved from 61.3% five-class accuracy to 65.7% following domain
adaptation. The best performing model (domain-adapted RoBERTa) achieved a
five-class accuracy of 77.4%, which was better than the physician's performance
(66%), the best vision model's performance (48.1), and was similar to the
multimodal model's performance (77.2). Domain adaptation improved the
performance of large language models in interpreting nuclear medicine text
reports.

本研究评估了基于 5 点 Deauville 评分预测的临床 18F‑氟脱氧葡萄糖 (FDG) PET/CT 报告的领域自适应技术在核医学中的价值，并发现该技术可提高大型语言模型解读核医学文本报告的性能。