Automating the Referral of Bone Metastases Patients With and Without the Use of Large Language Models

作者：Karl L. Sangwon, Xu Han, Anton S. Becker, Yuchong Zhang, Richard Ni, Jeff Zhang, Daniel Alexander Alber, Anton Alyakin, Masami Nakatsuka, Nicola Fabbri, Yindalon Aphinyanaphongs, Jonathan T. Yang, Abraham Chachoua, Douglas Kondziolka, Ilya Laufer, Eric K. Oermann · 发表于：Neurosurgery · 年份：2025 · DOI：10.1227/neu.0000000000003683 · 被引用次数：3 · 研究领域：Management of metastatic bone disease、Medical Imaging and Analysis、Artificial Intelligence in Healthcare and Education

BACKGROUND AND OBJECTIVES: Bone metastases, affecting more than 4.8% of patients with cancer annually, and particularly spinal metastases require urgent intervention to prevent neurological complications. However, the current process of manually reviewing radiological reports leads to potential delays in specialist referrals. We hypothesized that natural language processing (NLP) review of routine radiology reports could automate the referral process for timely multidisciplinary care of spinal metastases. METHODS: We assessed 3 NLP models-a rule-based regular expression (RegEx) model, GPT-4, and a specialized Bidirectional Encoder Representations from Transformers (BERT) model (NYUTron)-for automated detection and referral of bone metastases. Study inclusion criteria targeted patients with active cancer diagnoses who underwent advanced imaging (computed tomography, MRI, or positron emission tomography) without previous specialist referral. We defined 2 separate tasks: task of identifying clinically significant bone metastatic terms (lexical detection), and identifying cases needing a specialist follow-up (clinical referral). Models were developed using 3754 hand-labeled advanced imaging studies in 2 phases: phase 1 focused on spine metastases, and phase 2 generalized to bone metastases. Standard McRae's line performance metrics were evaluated and compared across all stages and tasks. RESULTS: In the lexical detection, a simple RegEx achieved the highest performance (sensitivi...