Guanchao Ye ¹ Guangyao Wu ^2* Yiying Li ^3* Chi Zhang ^1* Lili Qin ⁴ Jianlin Wu ⁴ Fan Jun ⁵ Yu Qi ⁶ Fan Yang ^2* Yongde Liao ^1*

• ¹ Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
• ² Department of Radiology, Wuhan Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
• ³ Department of Breast Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
• ⁴ Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, Liaoning Province, China
• ⁵ Department of Pathology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
• ⁶ Department of Thoracic Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China

Background To accurately identify spread through air spaces (STAS) in clinical stage IA lung adenocarcinoma, our study developed a non-invasive and interpretable biomarker combining clinical and radiomics features using preoperative CT. Methods The study included a cohort of 1325 lung adenocarcinoma patients from three centers, which was divided into four groups: a training cohort (n=930), a testing cohort (n=238), an external validation 1 cohort (n= 93), and 2 cohort (n=64). We collected clinical characteristics and semantic features, and extracted radiomics features. We utilized the LightGBM algorithm to construct prediction models using the selected features. Quantifying the contribution of radiomics features of CT to prediction model using Shapley additive explanations (SHAP) method. The models' performance was evaluated using metrics such as the area under the receiver operating characteristic curve (AUC), negative predictive value (NPV), positive predictive value (PPV), sensitivity, specificity, calibration curve, and decision curve analysis (DCA). Results In the training cohort, the clinical model achieved an AUC value of 0.775, the radiomics model achieved an AUC value of 0.836, and the combined model achieved an AUC value of 0.837. In the testing cohort, the AUC values of the models were 0.743, 0.755, and 0.768. In the external validation 1 cohort, the AUC values of the models were 0.717, 0.758, and 0.765, while in the external validation 2 cohort, 0.725, 0.726 and 0.746. The DeLong test results indicated that the combined model outperformed the clinical model (p<0.05). DCA indicated that the models provided a net benefit in predicting STAS. The SHAP algorithm explains the contribution of each feature in the model, visually demonstrating the impact of each feature on the model's decisions.The combined model has the potential to serve as a biomarker for predicting STAS using preoperative CT scans, determining the appropriate surgical strategy, and guiding the extent of resection.