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

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Τίτλος: Leveraging Large Language Models to Identify Lung Cancer Patients with Unregistered World Trade Center Disaster Exposure.
Συγγραφείς: Lo Cascio JN; From the NYU Grossman Long Island School of Medicine (GLISOM), Mineola, New York (J.N.L.C., N.M.); NYU Grossman School of Medicine (GSOM), New York, New York (C.J.O., R.B., F.H.); Department of Medicine, Division of Occupational Medicine, NYU GLISOM, Mineola, New York (M.W.); and Department of Medicine, NYU GLISOM, Mineola, New York (J.G.S., M.R.)., Mourikis N, Okpara CJ, Belenkaya R, Hussein F, Wilkenfeld M, Schneider JG, Rybstein M
Πηγή: Journal of occupational and environmental medicine [J Occup Environ Med] 2026 Oct 01; Vol. 68 (10), pp. 773-779. Date of Electronic Publication: 2026 May 22.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 9504688 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1536-5948 (Electronic) Linking ISSN: 10762752 NLM ISO Abbreviation: J Occup Environ Med Subsets: MEDLINE
Imprint Name(s): Publication: Hagerstown, MD : Lippincott Williams & Wilkins
Original Publication: Baltimore, MD : Williams & Wilkins, c1995-
Ιατρικοί όροι (MeSH): Lung Neoplasms*/genetics , Lung Neoplasms*/epidemiology , Lung Neoplasms*/mortality , Lung Neoplasms*/etiology , September 11 Terrorist Attacks* , Large Language Models*, Smoking/epidemiology ; New York City/epidemiology ; Humans ; Female ; Male ; Middle Aged ; Disease Progression ; Registries ; Aged ; Mutation ; Adult
Περίληψη: 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.
(Copyright © 2026 American College of Occupational and Environmental Medicine.)
Competing Interests: Conflict of Interest: The authors have no conflicts of interest to disclose.
Contributed Indexing: Keywords: 9/11; World Trade Center; large language models; primary lung cancer
Entry Date(s): Date Created: 20260522 Date Completed: 20260925 Latest Revision: 20260925
Update Code: 20260925
DOI: 10.1097/JOM.0000000000003760
PMID: 42168810
Βάση Δεδομένων: MEDLINE
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  Data: Leveraging Large Language Models to Identify Lung Cancer Patients with Unregistered World Trade Center Disaster Exposure.
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  Data: <searchLink fieldCode="AU" term="%22Lo+Cascio+JN%22">Lo Cascio JN</searchLink>; From the NYU Grossman Long Island School of Medicine (GLISOM), Mineola, New York (J.N.L.C., N.M.); NYU Grossman School of Medicine (GSOM), New York, New York (C.J.O., R.B., F.H.); Department of Medicine, Division of Occupational Medicine, NYU GLISOM, Mineola, New York (M.W.); and Department of Medicine, NYU GLISOM, Mineola, New York (J.G.S., M.R.).<br /><searchLink fieldCode="AU" term="%22Mourikis+N%22">Mourikis N</searchLink><br /><searchLink fieldCode="AU" term="%22Okpara+CJ%22">Okpara CJ</searchLink><br /><searchLink fieldCode="AU" term="%22Belenkaya+R%22">Belenkaya R</searchLink><br /><searchLink fieldCode="AU" term="%22Hussein+F%22">Hussein F</searchLink><br /><searchLink fieldCode="AU" term="%22Wilkenfeld+M%22">Wilkenfeld M</searchLink><br /><searchLink fieldCode="AU" term="%22Schneider+JG%22">Schneider JG</searchLink><br /><searchLink fieldCode="AU" term="%22Rybstein+M%22">Rybstein M</searchLink>
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  Data: <searchLink fieldCode="JN" term="%229504688%22">Journal of occupational and environmental medicine</searchLink> [J Occup Environ Med] 2026 Oct 01; Vol. 68 (10), pp. 773-779. <i>Date of Electronic Publication: </i>2026 May 22.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Lippincott+Williams+%26+Wilkins%22">Lippincott Williams & Wilkins </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>9504688 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1536-5948 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2210762752%22">10762752 </searchLink><i>NLM ISO Abbreviation: </i>J Occup Environ Med <i>Subsets: </i>MEDLINE
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  Data: <i>Publication</i>: Hagerstown, MD : Lippincott Williams & Wilkins<br /><i>Original Publication</i>: Baltimore, MD : Williams & Wilkins, c1995-
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  Label: MeSH Terms
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  Data: <searchLink fieldCode="MM" term="%22Lung+Neoplasms%22">Lung Neoplasms*</searchLink>/<searchLink fieldCode="MM" term="%22Lung+Neoplasms+genetics%22">genetics</searchLink> <br /><searchLink fieldCode="MM" term="%22Lung+Neoplasms%22">Lung Neoplasms*</searchLink>/<searchLink fieldCode="MM" term="%22Lung+Neoplasms+epidemiology%22">epidemiology</searchLink> <br /><searchLink fieldCode="MM" term="%22Lung+Neoplasms%22">Lung Neoplasms*</searchLink>/<searchLink fieldCode="MM" term="%22Lung+Neoplasms+mortality%22">mortality</searchLink> <br /><searchLink fieldCode="MM" term="%22Lung+Neoplasms%22">Lung Neoplasms*</searchLink>/<searchLink fieldCode="MM" term="%22Lung+Neoplasms+etiology%22">etiology</searchLink> <br /><searchLink fieldCode="MM" term="%22September+11+Terrorist+Attacks%22">September 11 Terrorist Attacks*</searchLink> <br /><searchLink fieldCode="MM" term="%22Large+Language+Models%22">Large Language Models*</searchLink><br /><searchLink fieldCode="MH" term="%22Smoking%22">Smoking</searchLink>/<searchLink fieldCode="MH" term="%22Smoking+epidemiology%22">epidemiology</searchLink> ; <searchLink fieldCode="MH" term="%22New+York+City%22">New York City</searchLink>/<searchLink fieldCode="MH" term="%22New+York+City+epidemiology%22">epidemiology</searchLink> ; <searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink> ; <searchLink fieldCode="MH" term="%22Female%22">Female</searchLink> ; <searchLink fieldCode="MH" term="%22Male%22">Male</searchLink> ; <searchLink fieldCode="MH" term="%22Middle+Aged%22">Middle Aged</searchLink> ; <searchLink fieldCode="MH" term="%22Disease+Progression%22">Disease Progression</searchLink> ; <searchLink fieldCode="MH" term="%22Registries%22">Registries</searchLink> ; <searchLink fieldCode="MH" term="%22Aged%22">Aged</searchLink> ; <searchLink fieldCode="MH" term="%22Mutation%22">Mutation</searchLink> ; <searchLink fieldCode="MH" term="%22Adult%22">Adult</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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.<br />Methods: Associations between exposure type and smoking history, cancer stage, mutation status, disease progression, and survival were statistically analyzed.<br />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).<br />Conclusions: This work highlights that large language model can capture a greater population of World Trade Center survivors, including genetics.<br /> (Copyright © 2026 American College of Occupational and Environmental Medicine.)
– Name: Abstract
  Label: Competing Interests
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  Data: Conflict of Interest: The authors have no conflicts of interest to disclose.
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  Data: <i>Keywords: </i>9/11; World Trade Center; large language models; primary lung cancer
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  Data: <i>Date Created: </i>20260522 <i>Date Completed: </i>20260925 <i>Latest Revision: </i>20260925
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  Data: 42168810
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=cmedm&AN=42168810
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        Text: English
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        StartPage: 773
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      – SubjectFull: Smoking epidemiology
        Type: general
      – SubjectFull: New York City epidemiology
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      – SubjectFull: Humans
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