Hua Guo ¹ Wei Zhao ² Chunsun Li ^2* Zhen Wu ^2* Ling Yu ^2* Miaoyu Wang ^2* Yuanhui Wei ³ Zirui Wang ^2* Shangshu Liu ^1* Yue Yin ^1* Zhen Yang ^2* Liangan Chen ^2*

• ¹ Medical School of Chinese PLA, Beijing, China
• ² Department of Pulmonary and Critical Care Medicine, People 's Liberation Army General Hospital, Beijing, China
• ³ School of Medicine, Nankai University, Tianjin, China

Background: Persistent ground-glass nodules (GGNs) carry a potential risk of malignancy, however, early diagnosis remained challenging. This study aimed to investigate the cut-off values of seven autoantibodies in patients with ground-glass nodules no more than 3cm, and to construct machine learning models to assess the diagnostic value of these autoantibodies. Methods: We collected peripheral blood specimens from a total of 698 patients. A total of 466 patients with ground-glass nodular lung adenocarcinoma no more than 3cm were identified as a case group based on pathological reports and imaging data, and control group (n=232) of patients consisted of 90 patients with benign nodules and 142 patients with health check-ups. Seven antibodies were quantified in the serum of all participants using enzyme-linked immunosorbent assay (ELISA), and the working characteristic curves of the subjects were plotted to determine the cut-off values of the seven autoantibodies related ground-glass nodular lung adenocarcinoma early. Subsequently, the patients were randomly divided into a training and test set at a 7:3 ratio. Eight machine-learning models were constructed to compare the diagnostic performances of multiple models. The model performances were evaluated using sensitivity, specificity, and the area under the curve (AUC). Results: The serum levels of the seven autoantibodies in case group were significantly higher than those in the control group (P < 0.05). The combination of the seven autoantibodies demonstrated a significantly enhanced diagnostic efficacy in identifying ground-glass nodular lung adenocarcinoma early when compared to the diagnostic efficacy of the autoantibodies when used respectively. The combined diagnostic approach of the seven autoantibodies exhibited a sensitivity of 84.05%, specificity of 91.85%, and AUC of 0.8870, surpassing the performance of each autoantibody used individually. Furthermore, we determined that Sparrow Search Algorithm-XGBoost (SSA-XGBOOST) had the best diagnostic performance among the models (AUC=0.9265), with MAGEA1, P53, and PGP9.5 having significant feature weight proportions. Conclusion: Our research assessed the diagnostic performance of seven autoantibodies in patients with ground-glass nodules for benign-malignant distinction, and the nodules are all no more than 3cm especially. This provides a non-invasive and highly discriminative method for the evaluation of ground-glass nodules in high-risk patients.