Novel Computed-Tomography-Based Transformer Models for the Noninvasive Prediction of PD-1 in Pre-Operative Settings

Simple Summary Obtaining the PD-1/PD-L1 status is conducive to observing the patient's response rate and constructing individualized immunotherapy strategies. However, biopsies are invasive in assessing the PD-1 status, entail sampling bias due to tumor heterogeneity, are expensive and a slow process, and introduce increased risks of complications. Our research explored a new model based on transformer and CT images to predict PD-1 use. We confirmed that our model can accurately predict the expression of PD-1 via the study of a cohort of 93 patients collected in West China Hospital. The promising diagnostic performance shows that our model is an effective and noninvasive classification method, providing a practical tool for predicting various receptors. The expression status of programmed cell death protein 1 (PD-1) in patients with hepatocellular carcinoma (HCC) is associated with the checkpoint blockade treatment responses of PD-1/PD-L1. Thus, accurately and preoperatively identifying the status of PD-1 has great clinical implications for constructing personalized treatment strategies. To investigate the preoperative predictive value of the transformer-based model for identifying the status of PD-1 expression, 93 HCC patients with 75 training cohorts (2859 images) and 18 testing cohorts (670 images) were included. We propose a transformer-based network architecture, ResTransNet, that efficiently employs convolutional neural networks (CNNs) and self-attention mechanisms to automatically acquire a persuasive feature to obtain a prediction score using a nonlinear classifier. The area under the curve, receiver operating characteristic curve, and decision curves were applied to evaluate the prediction model's performance. Then, Kaplan-Meier survival analyses were applied to evaluate the overall survival (OS) and recurrence-free survival (RFS) in PD-1-positive and PD-1-negative patients. The proposed transformer-based model obtained an accuracy of 88.2% with a sensitivity of 88.5%, a specificity of 88.9%, and an area under the curve of 91.1% in the testing cohort.