2024 World Conference on Lung Cancer (WCLC) - ePosters-EP.04D.02 An Innovative Evidence-Based Laboratory Medicine (EBLM) Test to Help Doctors in the Assessment of Lung Cancer

EP.04D.02 An Innovative Evidence-Based Laboratory Medicine (EBLM) Test to Help Doctors in the Assessment of Lung Cancer

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Lung cancer is the leading cause of cancer-related deaths globally, largely attributed to tobacco smoking and other risk factors like exposure to radon gas and asbestos. It includes two main types: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). A poster presented at the 2024 World Conference on Lung Cancer in San Diego, outlined the results of a study aiming to improve diagnostic assessments for lung cancer using an innovative, non-invasive test.<br /><br />This study by Calleja and Roca built on earlier research by Molina et al., integrating findings from nearly 1,152 new patients. Utilizing a new algorithm based on the Barcelona Criteria, which refines interpretations to minimize false positives, the research aimed to improve diagnostic accuracy using serum tumor markers (TM) like CA 15.3, CEA, CYFRA 21-1, and ProGRP. The enhanced algorithm notably increased specificity from 0.82 to 0.96, and markedly improved sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).<br /><br />The study confirmed or excluded lung cancer in patients referred for symptoms like cough or nodules using standard methods like CT and PET scans and aimed to establish these advanced TM analytical methods as viable alternatives to invasive procedures like biopsies. By applying algorithms such as the Evidence-Based Laboratory Medicine Algorithm (EBLMA), along with machine learning techniques, the study demonstrated improved diagnostic performance for identifying and differentiating between SCLC and NSCLC. Given these improvements, the study advocated for broader clinical trials with larger cohorts and additional diagnostic markers to further refine lung cancer diagnostics.<br /><br />Ultimately, the research highlights the significant potential for non-invasive methodologies in diagnosing lung cancer, offering promise for reducing unnecessary invasive procedures while improving early detection and management for at-risk populations.

Asset Subtitle

Sergio J Calleja

Meta Tag

Speaker Sergio J Calleja

Topic Screening and Early Detection

Keywords

lung cancer

diagnostic assessments

non-invasive test

small cell lung cancer

non-small cell lung cancer

serum tumor markers

Barcelona Criteria

machine learning

diagnostic accuracy

clinical trials