Real-World Outcomes of Patients with Advanced Epidermal Growth Factor Receptor-Mutated Non-Small Cell Lung Cancer in Canada Using Data Extracted by Large Language Model-Based Artificial Intelligence.

Real-world evidence for patients with advanced EGFR-mutated non-small cell lung cancer (NSCLC) in Canada is limited. This study's objective was to use previously validated DARWENTM artificial intelligence (AI) to extract data from electronic heath records of patients with non-squamous NSCLC at Unive...

Descripción completa

Detalles Bibliográficos
Publicado en:Current Oncology Vol. 31; no. 4; pp. 1947 - 1961
Autores principales: Moulson, Ruth, Law, Jennifer, Sacher, Adrian, Liu, Geoffrey, Shepherd, Frances A., Bradbury, Penelope, Eng, Lawson, Iczkovitz, Sandra, Abbie, Erica, Elia-Pacitti, Julia, Ewara, Emmanuel M., Mokriak, Viktoriia, Weiss, Jessica, Pettengell, Christopher, Leighl, Natasha B.
Formato: Journal Article
Publicado: MDPI Apr2024
Acceso en línea:Ver este registro en EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=176902583&site=ehost-live
header:
  @attributes:
    shortDbName: ccm
    uiTerm: 176902583
    longDbName: CINAHL Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    dissinfo:
    jinfo:
      jid:
        11980052
        5EKK
      jtl: Current Oncology
      issn: 11980052
      maglogo: N
    pubinfo:
      dt: Apr2024
      vid: 31
      iid: 4
      pid: 97109
      pub: MDPI
    artinfo:
      ui:
        176902583
        10.3390/curroncol31040146
        176902583
      ppf: 1947
      ppct: 14
      formats:
        fmt:
          – @attributes:
              type: T
          – @attributes:
              type: P
      tig:
        atl: Real-World Outcomes of Patients with Advanced Epidermal Growth Factor Receptor-Mutated Non-Small Cell Lung Cancer in Canada Using Data Extracted by Large Language Model-Based Artificial Intelligence.
      aug:
        au:
          Moulson, Ruth
          Law, Jennifer
          Sacher, Adrian
          Liu, Geoffrey
          Shepherd, Frances A.
          Bradbury, Penelope
          Eng, Lawson
          Iczkovitz, Sandra
          Abbie, Erica
          Elia-Pacitti, Julia
          Ewara, Emmanuel M.
          Mokriak, Viktoriia
          Weiss, Jessica
          Pettengell, Christopher
          Leighl, Natasha B.
        affil: Pentavere, 460 College Street, Toronto, ON M6G 1A1, Canada
      sug:
      ab: Real-world evidence for patients with advanced EGFR-mutated non-small cell lung cancer (NSCLC) in Canada is limited. This study's objective was to use previously validated DARWENTM artificial intelligence (AI) to extract data from electronic heath records of patients with non-squamous NSCLC at University Health Network (UHN) to describe EGFR mutation prevalence, treatment patterns, and outcomes. Of 2154 patients with NSCLC, 613 had advanced disease. Of these, 136 (22%) had common sensitizing EGFR mutations (cEGFRm; ex19del, L858R), 8 (1%) had exon 20 insertions (ex20ins), and 338 (55%) had EGFR wild type. One-year overall survival (OS) (95% CI) for patients with cEGFRm, ex20ins, and EGFR wild type tumours was 88% (83, 94), 100% (100, 100), and 59% (53, 65), respectively. In total, 38% patients with ex20ins received experimental ex20ins targeting treatment as their first-line therapy. A total of 57 patients (36%) with cEGFRm received osimertinib as their first-line treatment, and 61 (39%) received it as their second-line treatment. One-year OS (95% CI) following the discontinuation of osimertinib was 35% (17, 75) post-first-line and 20% (9, 44) post-second-line. In this real-world AI-generated dataset, survival post-osimertinib was poor in patients with cEGFR mutations. Patients with ex20ins in this cohort had improved outcomes, possibly due to ex20ins targeting treatment, highlighting the need for more effective treatments for patients with advanced EGFRm NSCLC.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
    refInfo:
    holdings:
      @attributes:
        islocal: N