免疫治疗是治疗非小细胞肺癌（NSCLC）的有效方法。免疫治疗的疗效因人而异，可能引起副作用，因此在手术前预测免疫治疗的疗效很重要。基于机器学习的放射学在预测NSCLC免疫治疗的疗效方面取得了成功。然而，大多数研究只考虑了个体患者的放射学特征，忽略了患者之间的相关性。此外，它们通常将不同特征拼接起来作为单视图模型的输入，没有考虑多种类型特征之间的复杂相关性。为此，本文提出了一种用于预测NSCLC免疫治疗疗效的多视图自适应加权图卷积网络（MVAW-GCN）。具体来说，我们根据图像提取的滤波类型将放射学特征分为几个视图。我们在每个视图中基于放射学特征和表型信息构建图形。引入注意机制自动分配每个视图的权重。考虑到放射学特征的视图共享和视图特定性知识，我们提出了可分离的图卷积，将最后一个卷积层的输出分解为视图共享和视图特定的输出。我们在学习过程中最大化不同视图之间的一致性和多样性。在107例NSCLC患者中进行了MVAW-GCN的评估，其中包括52例有效疗效患者和55例无效疗效患者。我们的方法实现了77.27％的准确率和0.7780的曲线下面积（AUC），证明了其在NSCLC免疫治疗疗效预测中的有效性。

Immunotherapy is an effective way to treat non-small cell lung cancer (NSCLC). The efficacy of immunotherapy differs from person to person and may cause side effects, making it important to predict the efficacy of immunotherapy before surgery. Radiomics based on machine learning has been successfully used to predict the efficacy of NSCLC immunotherapy. However, most studies only consider the radiomic features of the individual patient, ignoring the inter-patient correlations. Besides, they usually concatenate different features as the input of a single-view model, failing to consider the complex correlation among features of multiple types. To this end, we propose a multi-view adaptive weighted graph convolutional network (MVAW-GCN) for the prediction of NSCLC immunotherapy efficacy. Specifically, we divide the radiomic features into several views according to the type of the filtered images they extracted from. We construct a graph in each view based on the radiomic features and phenotypic information. An attention mechanism is introduced to automatically assign weights to each view. Considering the view-shared and view-specific knowledge of radiomic features, we propose separable graph convolution that decomposes the output of the last convolution layer into two components, i.e., the view-shared and view-specific outputs. We maximize the consistency and enhance the diversity among different views in the learning procedure. The proposed MVAW-GCN is evaluated on 107 NSCLC patients, including 52 patients with valid efficacy and 55 patients with invalid efficacy. Our method achieved an accuracy of 77.27% and an area under the curve (AUC) of 0.7780, indicating its effectiveness in NSCLC immunotherapy efficacy prediction.