To evaluate the feasibility of an arterial-phase computed tomography (CT)–based radiomics and habitat imaging model for noninvasive prediction of programmed death-ligand 1 (PD-L1) expression in non–small cell lung cancer (NSCLC).
Approach:
Study Design: Retrospective study including 246 patients with pathologically confirmed NSCLC who underwent arterial-phase CT.
Cohort Division: Patients were randomly divided into a training cohort (n = 172) and a validation cohort (n = 74).
Feature Extraction: Whole-tumor radiomics features were extracted from manually segmented lesions, and tumor habitats were generated using texture-based clustering.
Model Construction: Logistic regression was used to construct models incorporating whole-tumor radiomics, habitat imaging, and clinical variables.
Performance Assessment: Model performance was assessed using the area under the receiver operating characteristic curve (AUC) and decision curve analysis.
Key Findings:
Tumor maximum diameter and intratumoral necrosis were identified as independent clinical predictors of PD-L1 expression.
The habitat imaging model outperformed the whole-tumor radiomics model, achieving AUCs of 0.774 in the training cohort and 0.758 in the validation cohort.
The combined model demonstrated the best performance, with AUCs of 0.840 in the training cohort and 0.828 in the validation cohort.
Interpretation:
An arterial-phase CT–based habitat imaging model provides effective noninvasive prediction of PD-L1 expression in NSCLC.
Limitations:
The study is retrospective and may be subject to selection bias.
The sample size may limit the generalizability of the findings.
Conclusion:
The integration of radiomics and habitat imaging enhances the predictive capability for PD-L1 expression in NSCLC.
