Utilizing Clinicopathological and Radiomic Features for Risk Stratification of Lung Cancer Recurrence

To predict recurrence risk in patients with surgically resected non-small cell lung cancer (NSCLC) using radiomic analysis and clinicopathological factors.

293 patients with surgically resected stage IA-IIIA NSCLC were analyzed. Patients were randomly stratified into development and test cohorts. The development cohort was further divided into training and validation subsets for feature selection and model building, then applied to the test cohort. Pre-treatment computed tomography were segmented and 107 pyRadiomics features were extracted from intratumoral and peritumoral regions. Feature selection was performed using the maximum relevance minimum redundancy algorithm and Lasso regression. Clinical covariates were selected using univariable Cox regression. Radiomic, clinical, and radiomic-clinical models were constructed using a logistic regression classifier and evaluated using area under the curve (AUC). Kaplan-Meier curves for 3-year recurrence-free survival were compared between high-risk and low-risk groups using the log-rank test.

20 percent of patients experienced recurrence within 3 years. The radiomic-clinical model (AUC 0.77) outperformed the radiomic, clinical, and TNM stage models (AUC 0.76, 0.71, and 0.70, respectively) on the test set. Recurrence risk was five times higher in the high-risk group than the low-risk group (p<0.01) after stratification with the radiomic-clinical model. The most important features were regional lymph node metastases, the "GLDM Large Dependence Emphasis" texture, and the "Elongation" shape feature.

Radiomics analysis can be used in combination with clinicopathological features for effective recurrence risk stratification in patients with surgically resected NSCLC.