{
  "abstract": "Lung cancer causes more deaths than any other cancer because it is diagnosed late. The NLST and NELSON studies have demonstrated that lung cancer screening by CT scans reduce mortality from lung cancer by introducing a shift in diagnosis towards early-stage disease. However, the service provision of large-scale CT scanning is expensive and therefore can only be offered to people at the very highest risk. This means that only 40% people with lung cancer would have been eligible for CT screening prior to their diagnosis. Therefore, even with perfect regional coverage, lung cancer screening will not reach most people at risk. New tests are needed to expand screening to the wider population. Exhaled breath tests are attractive because they are non-invasive, with potential for community application. To this end, higher levels of exhaled reactive oxygen species (ROS) have been associated with lung cancer. However, translation to clinical practice has been limited by the high prevalence of other chronic diseases in people who smoke, which independently increase ROS. Recently, the VICTORY study 1 demonstrated that using a point-of-care device called Inflammacheck® to measure ROS and multiple breath parameters could distinguish different chronic respiratory conditions including lung cancer by using a machine learning algorithm. The ExPeL study (IRAS 336691) has demonstrated that this approach can be applied to populations relevant to screening. Lung cancer cases (n=23, 83% early stage) were separated from controls (n=11), consisting of ever smokers who were eligible for lung cancer screening, with 100% specificity and 80% sensitivity. Furthermore, ExPeL also included an exploratory mass spec analysis of exhaled breath condensate in lung cancer cases (n=19) and controls (n=23), which has identified four new molecules associated with lung cancer that could be incorporated into Inflammacheck® in the future to improve performance.Reference Fox L, D’Cruz LG, Chauhan M, Gates J, Szarazova N, DeVos R, Hicks A, Brown T, Stores R, Chauhan A. Diagnosis of respiratory conditions using exhaled breath condensate using inflammacheck(R) and advanced analytics: insights from the VICTORY study. J Breath Res. 2025;10:1088/1752–7163/add17c",
  "authors": [
    {
      "affiliations": [
        "University of Manchester, Manchester, UK"
      ],
      "name": "SB Knight"
    },
    {
      "affiliations": [
        "University of Manchester, Manchester, UK"
      ],
      "name": "D Patel"
    },
    {
      "affiliations": [
        "Portsmouth Hospitals University NHS Trust, Portsmouth, UK"
      ],
      "name": "L DCruz"
    },
    {
      "affiliations": [
        "Portsmouth Hospitals University NHS Trust, Portsmouth, UK"
      ],
      "name": "A Chauhan"
    },
    {
      "affiliations": [
        "Northern Care Alliance Foundation Trust, Manchester, UK"
      ],
      "name": "S Grundy"
    },
    {
      "affiliations": [
        "Northern Care Alliance Foundation Trust, Manchester, UK"
      ],
      "name": "N Diar-Bakerly"
    },
    {
      "affiliations": [
        "University of Manchester, Manchester, UK"
      ],
      "name": "W Ahmed"
    },
    {
      "affiliations": [
        "University of Manchester, Manchester, UK"
      ],
      "name": "D Trivedi"
    }
  ],
  "title": "S33 Harnessing exhaled breath for lung cancer early detection. The ExPeL study results",
  "uid": "36ec0910-7be3-5d06-b976-152a2857405c"
}