基于图神经网络的WSI癌症生存预测方法  

Graph neural network-based WSI cancer survival prediction method

作　　者：叶世杰 王永雄[1] Ye Shijie;Wang Yongxiong(Institute of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《光电工程》2024年第4期64-75,共12页Opto-Electronic Engineering

基　　金：上海市自然科学基金项目(22ZR1443700)。

摘　　要：全切片图像(Whole slide imaging,WSI)是癌症诊断和预后的关键依据,具有尺寸庞大、空间关系复杂以及风格各异等特点。由于其缺乏细节注释,传统的计算病理学方法难以处理肿瘤组织环境中的空间关系。本文提出了一种新型的基于图神经网络的WSI生存预测模型BC-GraphSurv。首先,采用迁移学习的预训练策略,构建WSI的病理关系拓扑结构,实现了对病理学图像特征和空间关系信息的有效提取。然后,采用GAT-GCN双分支结构进行预测,在图注意力网络中加入边属性和全局连接模块,同时引入图卷积网络分支补充局部细节,增强了对WSI风格差异的适应能力,能够有效利用拓扑结构处理空间关系,区分微病理环境。在WSI数据集TCGA-BRCA和TCGA-KIRC上进行的实验表明,BC-GraphSurv模型的一致性指数为0.7950和0.7458,相比于当前先进的生存预测模型提升了0.0409,充分证明了模型的有效性。Whole slide imaging(WSI)is the main basis for cancer diagnosis and prognosis,characterized by its large size,complex spatial relationships,and diverse styles.Due to its lack of detailed annotations,traditional computational pathology methods are difficult to handle WSI tasks.To address these challenges,this paper proposes a WSI survival prediction model based on graph neural networks,BC-GraphSurv.Specifically,we use transfer learning pre-training to extract features containing spatial relationship information and construct the pathological relationship topology of WSI.Then,the two branch structures of the improved graph attention network(GAT)and graph convolution network(GCN)are used to predict the extracted features.We combine edge attributes and global perception modules in GAT,while the GCN branch is used to supplement local details,which can achieve adaptability to WSI style differences and effectively utilize topological structures to handle spatial relationships and distinguish subtle pathological environments.Experimental results on the TCGA-BRCA dataset demonstrate BC-GraphSurv's effectiveness,achieving a C-index of 0.795-a significant improvement of 0.0409 compared to current state-of-the-art survival prediction models.This underscores its robust efficacy in addressing WSI challenges in cancer diagnosis and prognosis.

关 键 词：生存预测 图神经网络 迁移学习 注意力机制 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]