Abstract 6199: Defining and capturing progression in glioma by harnessing NLP in unstructured electronic health records

作者：Shreya Chappidi, Hawon Lee, Sarisha Jagasia, Casey Syal, George Zaki, Dylan Junkin, Nathan P. Golightly, Patrick J. Chitwood, Kevin Camphausen, Andra Krauze · 发表于：Cancer Research · 年份：2024 · DOI：10.1158/1538-7445.am2024-6199 · 被引用次数：3 · 研究领域：Artificial Intelligence in Healthcare、Radiomics and Machine Learning in Medical Imaging

Abstract Gliomas exhibit nearly uniform recurrence and poor prognosis. Management of high-grade tumors is surgery followed by chemoirradiation (CRT). Radiographic progression is assessed using contrast-enhanced MRI with reporting captured in Electronic Health Records (EHR). The ability to harness large scale EHR data is limited by the Response Assessment in Neuro-Oncology (RANO) tumor progression criteria, which defines progression as an aggregate of clinical and/or radiographic parameters requiring clinician judgement. As a result, glioma progression is not captured systematically in large data sets, limiting Progression Free Survival (PFS) as an outcome endpoint in data analysis. We developed an AI-based method using natural language processing to capture PFS parameters for analysis of MRI radiology reports. 1088 available brain MRI radiology reports for 81 patients with a pathologically confirmed diagnosis of glioblastoma (GBM) were aggregated in the NIH Integrated Data Analysis Platform. MRI reports were systematically analyzed and PFS manually captured using RANO criteria as ground truth. Common report terms indicating progression were compiled and included in task prompts applied to Large Language Model (LLM) Extraction Tools. The words progression (n=1047) and stable (n=1133) did not necessarily indicate either overall progression or stability in a report. Yet, in the 24 (30%) patients who progressed within 3 months of CRT, recurrence and mass effect occurred in 3.7% a...