Quantitative Radiological Features and Deep Learning for the Non-Invasive Evaluation of Programmed Death Ligand 1 Expression Levels in Gastric Cancer Patients: A Digital Biopsy Study

Published:November 08, 2022DOI:

      Rationale and objectives

      Programmed Death-Ligand 1 (PD-L1) is an important biomarker for patient selection of immunotherapy in gastric cancer (GC). This study aimed to construct and validate a non-invasive virtual biopsy system based on radiological features and clinical factors to predict the PD-L1 expression level in GC.

      Materials and methods

      217 patients who received gastrectomy for GC were consecutively enrolled in this study, with 157 patients from center 1 as the training cohort and 60 patients from center 2 as the external validation cohort. 1205 quantitative radiomics features were extracted from preprocessed pre-operative contrast-enhanced CT images of enrolled patients. A radiological signature was computed using a regression random forest model and was integrated with clinical factors in a multilayer perceptron. The performance of the digital biopsy system was evaluated by the receiver operating characteristic (ROC) curve and calibration curve in both the training and validation cohort.


      15 features were selected for the construction of radiological signature, which was significantly associated with expression levels of PD-L1 in both the training cohort (p<0.0001) and the external validation cohort (p<0.01). The hybrid deep learning model integrating the radiological signature and clinical factor could accurately distinguish GCs with high PD-L1 expression levels in both the training cohort (AUC = 0.806, 95%CI: 0.736–0.875) and the validation cohort (AUC = 0.784, 95%CI: 0.668–0.901).


      Our results indicate that the combination of deep learning and quantitative radiological features are potential approaches for the non-invasive evaluation of PD-L1 expression levels in GC. The digital biopsy system could provide valuable suggestive information for clinical decision-making of immunotherapy in GC.

      Key Words


      AUC (Area Under (the receiver operating characteristics) Curve), CEA (Carcinoma Embryonic Antigen), CECT (Contrast Enhanced CT), CPS (Combined Positive Score), GC (Gastric Cancer), GLM (Generalized Linear Model), HNSCC (Head and Neck Squamous Cell Carcinoma), ICC (Inter-class Correlation Coefficients), IHC (Immunohistochemistry), IncMSE (Increase in Mean Square Error), IncNP (Increase in Node Purity), MLP (Multilayer Perceptron), NSCLC (Non-Small Cell Lung Cancer), PD-1 (Programmed Cell Death 1), PD-L1 (Programmed Death Ligand 1), VOI (Volume of Interest), Radiomics feature types (), FOC (First order statistics), GLCM (Gray level co-occurrence matrix), GLDM (Gray level dependence matrix), GLRLM (Gray level run length matrix), GLSZM (Gray level size zone matrix), SB (Shape-Based)
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