PD-L1-targeted PET imaging for non-invasive assessment of immunotherapy response in lung cancer
Abstract
Background Immune checkpoint inhibitors have improved outcomes for several malignancies; however, there remains a lack of accurate, non-invasive methods to assess tumor PD-L1 expression levels and guide immunotherapy. This study aimed to evaluate the role of PD-L1-targeted positron emission tomography (PET) imaging in predicting immunotherapy response and prognosis in lung cancer. Methods Four healthy volunteers and 22 treatment-naïve lung cancer patients were prospectively enrolled and underwent [ 68 Ga]Ga-PDL1p PET imaging. All patients additionally completed paired baseline [ 18 F]fluorodeoxyglucose ([ 18 F]FDG) PET scans. Of the 22 patients, 17 received ≥3 cycles of immunotherapy combined with chemotherapy and underwent follow-up [ 18 F]FDG PET or CT examinations. The correlations of baseline [ 68 Ga]Ga-PDL1p and [ 18 F]FDG uptake with tumor PD-L1 expression were evaluated. Furthermore, the associations of tumor [ 68 Ga]Ga-PDL1p uptake, [ 18 F]FDG uptake, and PD-L1 expression with immunotherapy response were analyzed, along with their predictive values for immunotherapy efficacy and outcomes. Results Lesions with high PD-L1 expression exhibited significantly higher [ 68 Ga]Ga-PDL1p uptake than those with low expression (p=0.007), whereas [ 18 F]FDG uptake showed no significant difference (p=0.499). At baseline, [ 68 Ga]Ga-PDL1p uptake was significantly higher in responders than in non-responders (p=0.008), with an area under the receiver operating characteristic curve of 0.886. In contrast, neither [ 18 F]FDG uptake nor PD-L1 expression levels differed significantly between the two groups. Disease progression occurred in 23.5% of patients (4/17) by the final follow-up. Patients with higher [ 68 Ga]Ga-PDL1p uptake or higher [ 18 F]FDG uptake demonstrated significantly longer progression-free survival (PFS) than those with lower uptake (p=0.033 and p
Fields of Study & Subjects
Cancer Immunotherapy and Biomarkers
Lung Cancer Diagnosis and Treatment
Radiomics and Machine Learning in Medical Imaging
Publication details
- Journal / source
- Journal for ImmunoTherapy of Cancer
- Publisher
- BMJ
- Volume
- 14
- Issue
- 4
- Pages
- e013837–e013837
- Publication date
- 1 April 2026
- Language
- EN