2024 World Conference on Lung Cancer (WCLC) - ePosters-EP.04B.03 The Diagnostic Efficacy of Seven Lung Cancer Autoantibodies in Early Detection of Pulmonary Ground-Glass Nodules

EP.04B.03 The Diagnostic Efficacy of Seven Lung Cancer Autoantibodies in Early Detection of Pulmonary Ground-Glass Nodules

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The study conducted by Hua Guo and supervised by Liangan Chen and Zhen Yang, focuses on the diagnostic efficacy of seven autoantibodies (7-AAB) for the early detection of ground-glass nodular lung adenocarcinoma. This research aimed to enhance early-stage lung cancer detection through a non-invasive and efficient diagnostic model. The study involved 466 participants in the case group and 362 in the control group, recruited from eight medical centers in China between August 2021 and December 2023. <br /><br />The autoantibodies examined included P53, NY-ESO-1, CAGE, GBU4–5, Annexin 1, SOX2, PGP9.5, GAGE7, and MAGEA1. These autoantibodies were chosen due to their potential in providing significant insights into immune system responses in cancer detection. Using the enzyme-linked immunosorbent assay (ELISA) method, the study found that the serum concentration of these antibodies was significantly higher in patients with ground-glass nodular lung adenocarcinoma compared to healthy participants. <br /><br />For analysis and development of a diagnostic model, the study employed eight different algorithms, including Sparrow Search Algorithm XGBoost (SSA-XGBoost), which showed the best diagnostic discrimination, achieving an Area Under the Curve (AUC) of 0.9230, with sensitivity and specificity both greater than 90%. The model also identifies P53, MAGEA1, and PGP9.5 as the key contributing features in diagnosis.<br /><br />In conclusion, the research validated the use of the 7-AAB panel in the accurate diagnosis of lung adenocarcinoma, establishing optimal cut-off values for each antibody. The study emphasizes the potential of this method to improve early detection rates, thus possibly impacting the overall prognosis of patients by supporting timely intervention.

Asset Subtitle

Hua Guo

Meta Tag

Speaker Hua Guo

Topic Screening and Early Detection

Keywords

autoantibodies

lung adenocarcinoma

early detection

diagnostic model

ELISA

Sparrow Search Algorithm

XGBoost

P53

MAGEA1

PGP9.5