Abstract
Programmed cell death ligand-1 (PD-L1) is an ideal checkpoint for immunohistochemical detection. The method of obtaining PD-L1 expression through biopsy can impact the accurate assessment of PD-L1 expression due to the spatial and temporal heterogeneity of tumors. Because of the limited sample size, biopsies often give only a localized picture of the tumor. In this retrospective study, a total of 2,386 metabolic tumor volume (MTV) features were extracted from (18)F-FDG PET-CT images. A radiomics model was developed to holistically and non-invasively assess PD-L1 expression in patients with esophageal squamous cell carcinoma by identifying seven independent factors through feature screening. The radiomics model shows effective discrimination, with an area under the receiver operating characteristic curve of 0.888 [95% confidence interval (CI): 0.831-0.945] and 0.889 (95% CI: 0.706-1.000) for the training and validation cohorts, respectively. The results of the decision curve analysis demonstrated that utilizing the radiation model to forecast PD-L1 expression levels yielded more net benefits at threshold probabilities below 0.669. The clinical impact curves demonstrate that when the threshold probability is less than 0.501, the loss-to-benefit ratio is less than one in all cases.