Accurately predicting the survival rate of cancer patients is crucial for aiding clinicians in planning appropriate treatment, reducing cancer-related medical expenses, and significantly enhancing patients' quality of life. Multimodal prediction of cancer patient survival offers a more comprehensive and precise approach. However, existing methods still grapple with challenges related to missing multimodal data and information interaction within modalities. This paper introduces SELECTOR, a heterogeneous graph-aware network based on convolutional mask encoders for robust multimodal prediction of cancer patient survival. SELECTOR comprises feature edge reconstruction, convolutional mask encoder, feature cross-fusion, and multimodal survival prediction modules. Initially, we construct a multimodal heterogeneous graph and employ the meta-path method for feature edge reconstruction, ensuring comprehensive incorporation of feature information from graph edges and effective embedding of nodes. To mitigate the impact of missing features within the modality on prediction accuracy, we devised a convolutional masked autoencoder (CMAE) to process the heterogeneous graph post-feature reconstruction. Subsequently, the feature cross-fusion module facilitates communication between modalities, ensuring that output features encompass all features of the modality and relevant information from other modalities. Extensive experiments and analysis on six cancer datasets from TCGA demonstrate that our method significantly outperforms state-of-the-art methods in both modality-missing and intra-modality information-confirmed cases. Our codes are made available at https://github.com/panliangrui/Selector.

翻译：准确预测癌症患者生存率对于帮助临床医生制定合适治疗方案、降低癌症相关医疗费用以及显著提升患者生活质量至关重要。多模态癌症患者生存预测提供了更全面精准的方法。然而，现有方法仍面临多模态数据缺失以及模态内信息交互的挑战。本文提出SELECTOR——一种基于卷积掩码编码器的异构图感知网络，用于癌症患者生存的鲁棒多模态预测。SELECTOR包含特征边重构、卷积掩码编码器、特征交叉融合以及多模态生存预测模块。首先，我们构建多模态异构图，并采用元路径方法进行特征边重构，确保从图边中全面整合特征信息并有效嵌入节点。为减轻模态内特征缺失对预测精度的影响，我们设计了卷积掩码自编码器来对特征重构后的异构图进行处理。随后，特征交叉融合模块促进模态间信息交互，确保输出特征涵盖本模态全部特征及其他模态相关特征。在来自TCGA的六个癌症数据集上的大量实验与分析表明，本方法在模态缺失与模态内信息确认两种情况下均显著优于现有最优方法。我们的代码发布于https://github.com/panliangrui/Selector。