Self-supervised contrastive learning using CT images for PD-1/PD-L1 expression prediction in hepatocellular carcinoma

作者：Tianshu Xie, Yi Wei, Lifeng Xu, Qian Li, Feng Che, Qing Xu, Xuan Cheng, Minghui Liu, Minghui Liu, Meiyi Yang, Xiaomin Wang, Feng Zhang, Bin Song, Ming Liu, Ming Liu · 发表于：Frontiers in Oncology · 年份：2023 · DOI：10.3389/fonc.2023.1103521 · 被引用次数：17 · 研究领域：Cancer Immunotherapy and Biomarkers、Radiomics and Machine Learning in Medical Imaging、Ferroptosis and cancer prognosis

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 analyzed 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 withHCC, in particular the use of immune checkpoint inhibitors.