PG-TFNet: Transformer-based Fusion Network Integrating Pathological Images and Genomic Data for Cancer Survival Analysis

Survival analysis is crucial to the evaluation of cancer treatment options and deep learning-based methods integrating pathological images and genomic data have been used for prognosis prediction. However, the most methods are based on the analysis of pathological image patches, thus ignoring the morphological structure information at larger field-of-view and intrinsic relationships between patches. Meanwhile, the existing models fail to exploit the powerful representation learning capabilities of the neural networks for effective multimodal feature fusion of pathological images and genomic data. In this paper, we propose a novel transformer-based fusion network integrating pathological images and genomic data (PGTFNet) for cancer survival analysis. Specifically, we present a transformer-based feature fusion module for multi-scale pathological slides to fully exploit the intra-modality relationships between image patches at various fields of view. Moreover, in order to make effective inter-modality feature fusion of pathological images and genomic data, we introduce a cross-attention transformer module that can exchange feature representations of different modalities between two transformers branches. The PG-TFNet is performed on the colorectal cancer dataset from the Cancer Genome Atlas (TCGA), which contains paired whole-slide images and genomic data with ground truth survival data. The experimental results from a 10-fold cross validation demonstrate that the proposed PG-TFNet facilitates the prognosis prediction of colorectal cancer and shows superiority over the existing methods.