Study on Postoperative Survival Prediction Model for Non-Small Cell Lung Cancer: Application of Radiomics Technology Workflow Based on Multi-Organ Imaging Features and Various Machine Learning Algorithms

Hanlin Wang ¹ YUAN HONG ¹ Zimo Zhang ² Kang Cheng ² Bo Chen ¹ Renquan Zhang ^1*

• ¹ First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China
• ² Anhui Medical University, Hefei, Anhui Province, China

Objective: This study aims to construct an effective prediction model for the two-year postoperative survival probability of patients with non-small cell lung cancer (NSCLC). It particularly focuses on integrating radiomics features, including the erector spinae and whole-lung imaging features, to enhance the accuracy and stability of prognostic predictions. Materials and Methods: The study included 37 NSCLC patients diagnosed and surgically treated at the First Affiliated Hospital of 2 Anhui Medical University from January 2020 to December 2021. The average age of the patients was 59 years, with the majority being female and non-smokers. Additionally, CT imaging data from 98 patients were obtained from the The Cancer Imaging Archive (TCIA) public database. All imaging data were derived from preoperative chest CT scans and standardized using 3D Slicer software. The study extracted radiomic features from the tumor, whole lung, and erector spinae muscles of the patients and applied 11 machine learning algorithms to construct prediction models. Subsequently, the classification performance of all constructed models was compared to select the optimal prediction model. Results: Univariate Cox regression analysis showed no significant correlation between the collected clinical factors and patient survival time. In the external validation set, the K-Nearest Neighbors (KNN) model based on bilateral erector spinae features performed the best, with accuracy and AUC (Area Under the Curve) values consistently above 0.7 in both the training and external testing sets. Among the prognostic models based on whole-lung imaging features, the AdaBoost model also performed well, but its AUC value was below 0.6 in the external validation set, indicating overall classification performance still inferior to the KNN model based on erector spinae features. Conclusion: This study is the first to introduce erector spinae imaging features into lung cancer research, successfully developing a stable and well-performing prediction model for the postoperative survival of NSCLC patients. The research results provide new perspectives and directions for the application of radiomics in cancer research and emphasize the importance of incorporating multi-organ imaging features to improve 3 the accuracy and stability of prediction models.