  The rapidly emerging field of deep learning-based computational pathology has
shown promising results in utilizing whole slide images (WSIs) to objectively
prognosticate cancer patients. However, most prognostic methods are currently
limited to either histopathology or genomics alone, which inevitably reduces
their potential to accurately predict patient prognosis. Whereas integrating
WSIs and genomic features presents three main challenges: (1) the enormous
heterogeneity of gigapixel WSIs which can reach sizes as large as
150,000x150,000 pixels; (2) the absence of a spatially corresponding
relationship between histopathology images and genomic molecular data; and (3)
the existing early, late, and intermediate multimodal feature fusion strategies
struggle to capture the explicit interactions between WSIs and genomics. To
ameliorate these issues, we propose the Mutual-Guided Cross-Modality
Transformer (MGCT), a weakly-supervised, attention-based multimodal learning
framework that can combine histology features and genomic features to model the
genotype-phenotype interactions within the tumor microenvironment. To validate
the effectiveness of MGCT, we conduct experiments using nearly 3,600 gigapixel
WSIs across five different cancer types sourced from The Cancer Genome Atlas
(TCGA). Extensive experimental results consistently emphasize that MGCT
outperforms the state-of-the-art (SOTA) methods.
