Leveraging Large Language Models to Identify Lung Cancer Patients with Unregistered World Trade Center Disaster Exposure.

IF 1.4

Journal of occupational and environmental medicine Pub Date : 2026-10-01 Epub Date: 2026-05-22 DOI:10.1097/JOM.0000000000003760

Julia Nancy Lo Cascio, Nicholas Mourikis, Chinyere J Okpara, Rimma Belenkaya, Ferris Hussein, Marc Wilkenfeld, Jeffrey G Schneider, Marissa Rybstein

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Abstract

Objective: Leveraging large language models, we identified an unregistered subpopulation of individuals with World Trade Center-related exposure and lung cancer who were not previously captured in registries, and assessed how this exposure impacted patients' disease course.

Methods: Associations between exposure type and smoking history, cancer stage, mutation status, disease progression, and survival were statistically analyzed.

Results: The highest proportion of never-smokers was observed among residents, compared with First Responders and Commuters (19% and 24%; P = 0.005). Residents had more than twice the risk of disease progression (Hazard Ratio (HR) = 2.14, P = 0.008) and an elevated risk of death (HR = 2.43, P = 0.03). Only EGFR mutations were significantly associated with exposure type ( P = 0.01).

Conclusions: This work highlights that large language model can capture a greater population of World Trade Center survivors, including genetics.

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利用大型语言模型识别未登记的世界贸易中心灾难暴露肺癌患者。

目的：利用大型语言模型（LLM），我们确定了一个未登记的世界贸易中心相关暴露和肺癌个体亚群，这些个体以前未在登记中被捕获，并评估这种暴露如何影响患者的病程。方法：统计分析暴露类型与吸烟史、癌症分期、突变状态、疾病进展和生存率的关系。结果：与第一响应者和通勤者相比，居民中不吸烟者的比例最高（19%和24%；p = 0.005）。居民疾病进展风险（HR = 2.14, p = 0.008）和死亡风险升高（HR = 2.43, p = 0.03）的两倍以上。只有EGFR突变与暴露类型显著相关（p = 0.01）。结论：这项工作强调LLM可以捕获更多的WTC幸存者，包括遗传学。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Journal of occupational and environmental medicine

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