Enhancing survival predictions in lung cancer with cystic airspaces: a multimodal approach combining clinical and radiomic features

Abstract

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 χ² 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.

Keywords: lung cancer with cystic airspaces; predictive model; radiomics; survival; volume doubling time.

Figure 1

A 54-year-old male patient with a nodule with cystic airspaces identified during initial examination (A). Follow-up CT at 12 months showed increased nodule size (B). The final examination’s 3D tumor model displays the cystic area in yellow and the tumor in red (C).

Figure 2

Receiver operating characteristic (ROC) curves in the training set for predicting the 5-year survival rate of LCCA using the Tumor Volume Doubling Time model, Radiomics model, and Fusion model. The Fusion model achieves the largest area under the curve (AUC = 0.905) among the three.

Figure 3

ROC curves in the validation set for predicting the 5-year survival rate of LCCA using the Tumor Volume Doubling Time model, Radiomics model, and Fusion model. The Fusion model again shows the highest AUC (AUC = 0.895) in comparison.

Figure 4

illustrate the nomogram, calibration curve, and decision curve for predicting the five-year survival rate of LCCA using the fusion model based on VDT, radiomics, and clinical imaging features. (A) presents the nomogram. (B) shows the calibration curve, indicating minimal deviation and demonstrating the high reliability of the fusion model. (C) displays the decision curve.