Noninvasive Prediction of Programmed Cell Death Protein-Ligand 1 Expression in Locally Advanced Non-small Cell Lung Cancer by 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography-Based Metabolic Habitats: A Multicenter Radiomic and Biological Study

Background Programmed cell death protein-ligand 1 (PD-L1) expression is an important marker for immunotherapy in locally advanced non-small cell lung cancer (LA-NSCLC). PD-L1 expression has a bi-directional positive feedback relationship with glycolysis status. Objective This study aimed to develop a metabolic habitat model based on 18 F-fluorodeoxyglucose positron emission tomography/computed tomography ( 18 F-FDG PET/CT) images to predict PD-L1 expression levels in patients with LA-NSCLC, and to explore relevant biological characteristics. Methods We included 219 patients from two independent centers and divided them into the training ( n = 175) and testing ( n = 44) cohorts. Tumors were segmented into four spatially distinct, biologically similar metabolic habitat subregions using the Otsu method. Radiomic characteristics and metabolic parameters were extracted from each habitat and used to generate multiple predictive models based on the Extra Trees classifier. Data from 1043 patients in The Cancer Genome Atlas database were used to analyze the genes associated with PD-L1 expression in NSCLC. Results The metabolic habitat model exhibited the highest performance, with area under the curve values of 0.833 and 0.786 in the training and testing cohorts, respectively, outperforming other models. Subregion analysis revealed that high-glycolytic/high-density habitats (PET High –CT High ) exhibited the highest metabolic characteristics, and their spatial distribution correlated positively with PD-L1 expression. Four genes ( IFNG , IL2RA , HK3 , and MYCN ) were associated with PD-L1 expression in glycolysis gene correlation analysis. Conclusions The metabolic habitat model based on 18 F-FDG PET/CT enables noninvasive prediction of PD-L1 expression in LA-NSCLC. Its interpretability is enhanced by spatial habitat distribution, thereby advancing its potential for clinical translation.