Predicting PD-L1 in Lung Adenocarcinoma Using ¹⁸F-FDG PET/CT Radiomic Features

Background/Objectives: This study aims to retrospectively analyze the clinical and imaging data of 101 patients with lung adenocarcinoma who underwent [¹⁸F]FDG PET/CT examination and were pathologically confirmed in the Department of Nuclear Medicine at Peking University Cancer Hospital. This study explores the predictive value and important features of [¹⁸F]FDG PET/CT radiomics for PD-L1 expression levels in lung adenocarcinoma patients, assisting in screening patients who may benefit from immunotherapy. Methods: 101 patients with histologically confirmed lung adenocarcinoma who received pre-treatment [¹⁸F] FDG PET/CT were included. Among them, 44 patients were determined to be PD-L1 positive and 57 patients were determined to be PD-L1 negative based on immunohistochemical assays. Clinical data, PET/CT radiomics parameters, conventional metabolic parameters, and observed CT characteristics were included in the modeling. Random Forest was used in feature denoising, while Forward Stepwise Regression and the Least Absolute Shrinkage and Selection Operator were used in feature selection. Models based on Tree, Discriminant, Logistic Regression, and Support Vector Machine were trained and evaluatedto explore the value of clinical data, PET/CT radiomics parameters, conventional metabolic parameters, and observed CT characteristics. Results: All models showed some predictive ability in distinguishing PD-L1 positive from PD-L1 negative samples. Among the multimodal imaging, clinical data were incorporated into the models, with clinical stage and gender selected by Forward Stepwise Regression, while clinical stage, smoking history, and gender were selected by LASSO. When incorporating clinical data and thin-section CT-derived images into the models, nodular type, spiculation, and CT Shape Flatness were selected by Forward Stepwise Regression, while nodular type and spiculation were selected by LASSO. When incorporating clinical data, PET/CT radiomics, observed CT characteristics, and conventional metabolic information. Forward Stepwise Regression selected TLGlean, MTV, nodule component, PET Shape Sphericity, while LASSO selected SULmax, MTV, nodular type, PET Shape Sphericity, and spiculation. Conclusions: The integration of clinical data, PET/CT radiomics, and conventional metabolic parameters effectively predicted PD-L1 expression, thereby assisting the selection of patients who would benefit from immunotherapy. Observed CT characteristics and conventional metabolic information play an important role in predicting PD-L1 expression levels.