4.3 Article

Predicting PD-L1 expression status in patients with non-small cell lung cancer using [F-18]FDG PET/CT radiomics

Journal

EJNMMI RESEARCH
Volume 13, Issue 1, Pages -

Publisher

SPRINGER
DOI: 10.1186/s13550-023-00956-9

Keywords

Non-small cell lung cancer; [F-18]FDG PET; CT; Radiomics; PD-L1

Ask authors/readers for more resources

This study aimed to evaluate the predictive power of PET/CT-based radiomics features in predicting PD-L1 expression status in NSCLC patients. Through analyzing 334 NSCLC patients who underwent [F-18]FDG PET/CT imaging, radiomics features were extracted and radiomics model, clinical model, and combined model were constructed. The results showed that the radiomics model had better predictive performance than the clinical model, and the combined model had higher AUC values, indicating that PET/CT-based radiomics features can be used to preselect patients who may benefit from PD-1/PD-L1-based immunotherapy.
BackgroundIn recent years, immune checkpoint inhibitor (ICI) therapy has greatly changed the treatment prospects of patients with non-small cell lung cancer (NSCLC). Among the available ICI therapy strategies, programmed death-1 (PD-1)/programmed death ligand-1 (PD-L1) inhibitors are the most widely used worldwide. At present, immunohistochemistry (IHC) is the main method to detect PD-L1 expression levels in clinical practice. However, given that IHC is invasive and cannot reflect the expression of PD-L1 dynamically and in real time, it is of great clinical significance to develop a new noninvasive, accurate radiomics method to evaluate PD-L1 expression levels and predict and filter patients who will benefit from immunotherapy. Therefore, the aim of our study was to assess the predictive power of pretherapy [F-18]-fluorodeoxyglucose ([F-18]FDG) positron emission tomography/computed tomography (PET/CT)-based radiomics features for PD-L1 expression status in patients with NSCLC.MethodsA total of 334 patients with NSCLC who underwent [F-18]FDG PET/CT imaging prior to treatment were analyzed retrospectively from September 2016 to July 2021. The LIFEx7.0.0 package was applied to extract 63 PET and 61 CT radiomics features. In the training group, the least absolute shrinkage and selection operator (LASSO) regression model was employed to select the most predictive radiomics features. We constructed and validated a radiomics model, clinical model and combined model. Receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were used to evaluate the predictive performance of the three models in the training group and validation group. In addition, a radiomics nomogram to predict PD-L1 expression status was established based on the optimal predictive model.ResultsPatients were randomly assigned to a training group (n = 233) and a validation group (n = 101). Two radiomics features were selected to construct the radiomics signature model. Multivariate analysis showed that the clinical stage (odds ratio [OR] 1.579, 95% confidence interval [CI] 0.220-0.703, P < 0.001) was a significant predictor of different PD-L1 expression statuses. The AUC of the radiomics model was higher than that of the clinical model in the training group (0.706 vs. 0.638) and the validation group (0.761 vs. 0.640). The AUCs in the training group and validation group of the combined model were 0.718 and 0.769, respectively.ConclusionPET/CT-based radiomics features demonstrated strong potential in predicting PD-L1 expression status and thus could be used to preselect patients who may benefit from PD-1/PD-L1-based immunotherapy.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.3
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available