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

ABSTRACT:

Rationale and objectives

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

Materials and methods

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 usin

SUBMITTER: Ho WLJ 

PROVIDER: S-EPMC12667037 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature