LLM-Driven Extraction of NI-RADS and Imaging Tumor Characteristics to Enhance Oropharyngeal Cancer Survivorship Surveillance

作者：Wenye Song, Lina Shbita, Isabelle Jia Hui Jang, Olga Starostina, Ryan Lewis, Ariana Sahli, Warren Robert Floyd, Md Mahin, Waree Rinsurongkawong, Carly E.A. Barbon, Stephen Y. Lai, J. Jack Lee, Komal Shah, Melissa Chen, Katherine A. Hutcheson, Clifton D. Fuller, Amy C. Moreno · 发表于：medRxiv · 年份：2026 · DOI：10.64898/2026.06.11.26355483 · 研究领域：Head and Neck Cancer Studies、Radiomics and Machine Learning in Medical Imaging、Esophageal Cancer Research and Treatment

Purpose: Radiologic surveillance is essential for oropharyngeal cancer (OPC) survivors, guiding recurrence detection and follow-up strategies. The Neck Imaging Reporting and Data System provides a standardized framework for post-treatment risk reporting at both the primary tumor site (pNI-RADs) and cervical lymph nodes (nNI-RADS). Comprehensive surveillance additionally requires assessment of disease status, including the primary tumor, nodal involvement, and distant metastases. These clinical results are often embedded as unstructured data within free-text radiology reports. We hypothesized that a large language model (LLM) can reliably extract NI-RADS score criteria and summarize key imaging features from unstructured radiology text, achieving high concordance with expert review. Methods: Previously untreated OPC patients who received definitive cancer therapy were identified. Eligible imaging reports included post-treatment head and neck CT, MRI, or FDG PET/CT scans containing narrative and impression text. Examinations lacking narrative or impression text, containing pre-existing NI-RADS annotations, or involving non-surveillance imaging modalities were excluded. A total of 200 reports were randomly selected from 7,076 eligible examinations for manual abstraction using a three-reviewer consensus framework to establish a reference dataset. Using the Palantir Foundry Pipeline Builder, a GPT-5-based LLM was deployed to extract pNI-RADS and nNI-RADS scores, and key imaging fe...