  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.
