Self-supervised contrastive learning using CT images for PD-1/PD-L1 expression prediction in hepatocellular carcinoma - Xie, Tianshu; Wei, Yi; Xu, Lifeng; Li, Qian; Che, Feng; Xu, Qing; Cheng, Xuan; Liu, Minghui; Yang, Meiyi; Wang, Xiaomin; Zhang, Feng; Song, Bin; et al. (Frontiers in Oncology, 2023)
Background and purpose: Programmed cell death protein-1 (PD-1) and programmed cell death-ligand-1 (PD-L1) expression status, determined by immunohistochemistry (IHC) of specimens, can discriminate patients with hepatocellular carcinoma (HCC) who can derive the most benefits from immune checkpoint inhibitor (ICI) therapy. A non-invasive method of measuring PD-1/PD-L1 expression is urgently needed for clinical decision support. Materials and methods: We included a cohort of 87 patients with HCC from the West China Hospital and analysed 3094 CT images to develop and validate our prediction model. We propose a novel deep learning-based predictor, Contrastive Learning Network (CLNet), which is trained with self-supervised contrastive learning to better extract deep representations of computed tomography (CT) images for the prediction of PD-1 and PD-L1 expression. Results: Our results show that CLNet exhibited an AUC of 86.56% for PD-1 expression and an AUC of 83.93% for PD-L1 expression, outperforming other deep learning and machine learning models. Conclusions: We demonstrated that a non-invasive deep learning-based model trained with self-supervised contrastive learning could accurately predict the PD-1 and PD-L1 expression status, and might assist the precision treatment of patients with HCC, in particular the use of immune checkpoint inhibitors.