癌症复发是治疗后患者死亡的主要原因之一，表明癌细胞的侵袭性增加并且难以治愈。提高患者生存率的关键一步是准确预测复发状态并给予适当的治疗。全切片图像（WSI）是数字病理学领域中常见的图像数据类型，包含高分辨率组织信息。此外，原发性肿瘤的 WSI 包含与肿瘤细胞生长直接相关的微环境信息。有效利用这些微环境信息。首先，我们将组织病理学图像的微环境特征表示为紧凑图。其次，这项工作旨在开发一种增强的轻量级图神经网络，称为自适应图聚类网络（AGCNet），用于预测癌症复发。在癌症基因组图谱 (TCGA) 的三个癌症数据集上进行实验，AGCNet 在 BLCA 中的准确率达到 81.81%，在 PAAD 中达到 69.66%，在 STAD 中达到 81.96%。这些结果表明AGCNet是预测癌症复发的有效模型，有望应用于临床应用。版权所有©2023 Elsevier Ltd.保留所有权利。

Cancer recurrence is one of the primary causes of patient mortality following treatment, indicating increased aggressiveness of cancer cells and difficulties in achieving a cure. A critical step to improve patients' survival is accurately predicting recurrence status and giving appropriate treatment. Whole Slide Images (WSIs) are a common type of image data in the field of digital pathology, containing high-resolution tissue information. Furthermore, WSIs of primary tumors contain microenvironmental information directly associated with the growth of tumor cells. To effectively utilize this microenvironmental information. Firstly, we represented microenvironmental features of histopathological images as compact graphs. Secondly, this work aims to develop an enhanced lightweight graph neural network called the Adaptive Graph Clustering Network (AGCNet) for predicting cancer recurrence. Experiments are conducted on three cancer datasets from The Cancer Genome Atlas (TCGA), and AGCNet achieved an accuracy of 81.81% in BLCA, 69.66% in PAAD, and 81.96% in STAD. These results indicated that AGCNet is an effective model for predicting cancer recurrence and is expected to be applied in clinical applications.Copyright © 2023 Elsevier Ltd. All rights reserved.