Predicting PD-L1 status in NSCLC patients using deep learning radiomics based on CT images

Abstract

Radiomics refers to the utilization of automated or semi-automated techniques to extract and analyze numerous quantitative features from medical images, such as computerized tomography (CT) or magnetic resonance imaging (MRI) scans. This study aims to develop a deep learning radiomics (DLR)-based approach for predicting programmed death-ligand 1 (PD-L1) expression in patients with non-small cell lung cancer (NSCLC). Data from 352 NSCLC patients with known PD-L1 expression were collected, of which 48.29% (170/352) were tested positive for PD-L1 expression. Tumor regions of interest (ROI) were semi-automatically segmented based on CT images, and DL features were extracted using Residual Network 50. The least absolute shrinkage and selection operator (LASSO) algorithm was used for feature selection and dimensionality reduction. Seven algorithms were used to build models, and the most optimal ones were identified. A combined model integrating DLR with clinical data was also developed. The predictive performance of each model was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve analysis. The DLR model, based on CT images, demonstrated an AUC of 0.85 (95% confidence interval (CI), 0.82-0.88), sensitivity of 0.80 (0.74-0.85), and specificity of 0.73 (0.70-0.77) for predicting PD-L1 status. The integrated model exhibited superior performance, with an AUC of 0.91 (0.87-0.95), sensitivity of 0.85 (0.82-0.89), and specificity of 0.75 (0.72-0.80). Our findings indicate that the DLR model holds promise as a valuable tool for predicting the PD-L1 status in patients with NSCLC, which can greatly assist in clinical decision-making and the selection of personalized treatment strategies.

Keywords: Deep learning; Non-small cell lung cancer (NSCLC); Programmed death-ligand 1(PD-L1)..

Fig. 1

Flowchart of NSCLC patient selection.

Fig. 2

Workflow of the DLR analysis.

Fig. 3

Selection of radiomics features. The LASSO was used to filter the most relevant features. (A) Selection of tuning parameter (λ). (B) Coefficient of each selected feature. LASSO: least absolute shrinkage and selection operator, MSE: mean square error.

Fig. 4

ROC curves for the DLR models and the combined model in predicting the expression statuses of PD-L1. (A) ROC curves for the DLR models. (B) ROC curve for the combined model.

Fig. 5

Visualization of 2 patient examples. Each example displays a gray-scale CT image alongside its corresponding heat map. Within these heat maps, the red region signifies a higher weight, which can be interpreted using the color bar located on the right-hand side.