Enhancing Survival Predictions in Lung Cancer with Cystic Airspaces: A Multimodal Approach Combining Clinical and Radiomic Features

Liang Yin ¹ jing wang ^2* pingyou fu ³ lu xing ³ yuan liu ³ zongchang li ¹ jie gan ^1*

• ¹ Medical Imaging, Shandong Provincial Third Hospital, Jinan, Shandong Province, China
• ² Internet Healthcare, Shandong Provincial Third Hospital, Jinan, Shandong Province, China
• ³ Radiology Department, Shandong Yellow River Hospital, jinan, China

Objective: To enhance the prognostic assessment and management of lung cancer with cystic airspaces (LCCA) by integrating temporal clinical and phenotypic dimensions of tumor growth. Patients and Methods: A retrospective analysis was conducted on LCCA patients treated at two hospitals. Clinical and imaging characteristics were analyzed using the independent samples t-test, Mann-Whitney U test, and χ 2 test. Features with significant differences were further analyzed using multivariate Cox regression to identify independent prognostic factors. Radiomic features were extracted from CT images, and volume doubling time (VDT) was calculated from two follow-up scans. Separate predictive models were constructed based on radiomic features and VDT. A fusion model integrating radiomic features, VDT, and independent clinical prognostic factors was developed. Model performance was evaluated using receiver operating characteristic curve and the area under the curve, with DeLong's test used for comparison. Results: A total of 193 patients were included, with an average survival time of 48.5 months. Significant differences were found between survivors and non-survivors in age, smoking status, chronic obstructive pulmonary disease, and tumor volume (P < 0.05). Multivariate Cox analysis identified smoking and chronic obstructive pulmonary disease as independent risk factors (P = 0.028 and P = 0.013). The VDT for survivors was 421 (298 582.5) days compared to 334.5±106.1 days for non-survivors (Z = -3.330, P = 0.001). In the validation set, the area under the curve for the VDT model was 0.805, for the radiomic model 0.717, and for the fusion model 0.895, demonstrating the highest predictive performance (P < 0.05). Conclusion: Integrating VDT, radiomics, and clinical imaging features into a fusion model improves the accuracy of predicting the five-year survival rate for LCCA patients, enhancing personalized and precise cancer treatment.