Objective This study explored the predictive value of 18 F-fluorodeoxyglucose (FDG) PET/computed tomography (CT) radiomics for assessing programmed death-ligand 1 (PD-L1) expression in non-small cell lung cancer (NSCLC), aiming to noninvasively evaluate PD-L1 status and assist in selecting patients for immunotherapy. Methods We retrospectively analyzed 163 NSCLC patients with pretreatment 18 F-FDG PET/CT scans, randomly assigning them into training ( n = 130) and validation ( n = 33) cohorts. Optimal radiomics features were selected via least absolute shrinkage and selection operator and combined with clinical factors to construct five predictive models: CT, PET, radiomics, clinical, and a combined model. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). Results All models showed predictive ability for PD-L1 expression. The combined model demonstrated superior performance, with AUCs of 0.839 95% confidence interval (CI): 0.771–0.908 in training and 0.782 (95% CI: 0.610–0.954) in validation. Calibration curves indicated good agreement between predicted and observed probabilities (Brier scores: 0.163 and 0.191, respectively). DCA confirmed the highest net clinical benefit for the combined model. Conclusion The multimodal combined model, integrating PET/CT radiomics with clinical factors, shows significant potential for noninvasively predicting PD-L1 expression in NSCLC, offering a novel strategy for precise patient selection for anti-PD-L1 immunotherapy.
Liu et al. (2026) studied this question.