Development of Biologically Interpretable Multimodal Deep Learning Model for Cancer Prognosis Prediction

Abstract

Robust cancer prognostication can enable more effective patient care and management, which may potentially improve health outcomes. Deep learning has proven to be a powerful tool to extract meaningful information from cancer patient data. In recent years it has displayed promise in quantifying prognostication by predicting patient risk. However, most current deep learning-based cancer prognosis prediction methods use only a single data source and miss out on learning from potentially rich relationships across modalities. Existing multimodal approaches are challenging to interpret in a biological or medical context, limiting real-world clinical integration as a trustworthy prognostic decision aid. Here, we developed a multimodal modeling approach that can integrate information from the central modalities of gene expression, DNA methylation, and histopathological imaging with clinical information for cancer prognosis prediction. Our multimodal modeling approach combines pathway and gene-based sparsely coded layers with patch-based graph convolutional networks to facilitate biological interpretation of the model results. We present a preliminary analysis that compares the potential applicability of combining all modalities to uni- or bi-modal approaches. Leveraging data from four cancer subtypes from the Cancer Genome Atlas, results demonstrate the encouraging performance of our multimodal approach (C-index=0.660 without clinical features; C-index=0.665 with clinical features) across four cancer subtypes versus unimodal approaches and existing state-of-the-art approaches. This work brings insight to the development of interpretable multimodal methods of applying AI to biomedical data and can potentially serve as a foundation for clinical implementations of such software. We plan to follow up this preliminary analysis with an in-depth exploration of factors to improve multimodal modeling approaches on an in-house dataset.