Xie Tianshu, Wei Yi, Xu Lifeng, Li Qian, Che Feng, Xu Qing, Cheng Xuan, Liu Minghui, Yang Meiyi, Wang Xiaomin, Zhang Feng, Song Bin, Liu Ming
Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China.
Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
Front Oncol. 2023 Mar 3;13:1103521. doi: 10.3389/fonc.2023.1103521. eCollection 2023.
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.
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.
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.
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.
通过标本免疫组织化学（IHC）测定的程序性细胞死亡蛋白1（PD-1）和程序性细胞死亡配体1（PD-L1）表达状态，可鉴别出能从免疫检查点抑制剂（ICI）治疗中获益最大的肝细胞癌（HCC）患者。临床决策支持迫切需要一种测量PD-1/PD-L1表达的非侵入性方法。
我们纳入了来自华西医院的87例HCC患者队列，并分析了3094张CT图像以开发和验证我们的预测模型。我们提出了一种基于深度学习的新型预测器，对比学习网络（CLNet），它通过自监督对比学习进行训练，以更好地提取计算机断层扫描（CT）图像的深度特征，用于预测PD-1和PD-L1表达。
我们的结果表明，CLNet对PD-1表达的AUC为86.56%，对PD-L1表达的AUC为83.93%，优于其他深度学习和机器学习模型。
我们证明，通过自监督对比学习训练的基于深度学习的非侵入性模型可以准确预测PD-1和PD-L1表达状态，并可能有助于HCC患者的精准治疗，特别是免疫检查点抑制剂的使用。
