Development and validation of a predictive model combining radiomics and deep learning features for Spread Through Air Spaces in Stage T1 Non-Small Cell Lung Cancer: A Multicenter Study

Pengliang Xu^1*Huanming Yu¹Wenjian Xing²Shiyu Zhang³Haihua Hu⁴Wenhui Li¹Dan Jia¹Shengxu Zhi¹Peng Xiuhua^1*

• ¹The First People's Hospital of Huzhou, Huzhou, China
• ²Nanxun People's Hospital of Huzhou, Huzhou, Zhejiang Province, China
• ³Wuxi Xishan People′s Hospital, Jangsu, China
• ⁴Mingzhou Hospital, Zhejiang University, Ningbo, Zhejiang Province, China

Purpose The goal of this paper is to compare the effectiveness of three deep learning models (2D, 3D, and 2.5D), three radiomics models (INTRA, Peri2mm, and Fusion2mm), and a combined model in predicting the spread through air spaces (STAS) in non-small cell lung cancer (NSCLC) to identify the optimal model for clinical surgery planning.We included 480 patients who underwent surgery at four centers between January 2019 and August 2024, dividing them into a training cohort, an internal test cohort, and an external validation cohort. We extracted deep learning features using the ResNet50 algorithm. Least absolute shrinkage selection operator(Lasso) and spearman rank correlation were utilized to choose features. Extreme Gradient Boosting (XGboost) was used to execute deep learning and radiomics. Then, a combination model was developed, integrating both sources of data. Result The combined model showed outstanding performance, with an area under the receiver operating characteristic curve (AUC) of 0.927 (95% CI 0.870 -0.984) in the test set and 0.867 (95% CI 0.819 -0.915) in the validation set. This model significantly distinguished between high-risk and low-risk patients and demonstrated significant advantages in clinical application. Conclusion: The combined model is adequate for preoperative prediction of STAS in patients with stage T1 NSCLC, outperforming the other six models in predicting STAS risk.