Histopathology encodes the transcriptome: Image-based deep learning classification of colorectal cancer molecular subtypes

Abstract

Molecular subtyping of colorectal cancer (CRC) provides valuable insights into tumor heterogeneity and has the potential to guide treatment strategies. However, its clinical integration remains limited, largely due to the cost and complexity of RNA sequencing required for subtype assignment. In this study, we investigated the feasibility of using deep learning to classify CRC consensus molecular subtypes (CMSs) directly from histopathology images. We utilized diagnostic whole-slide images and corresponding RNA-seq data from the TCGA COAD and READ cohorts, focusing on tumor regions. These tumor regions were tessellated into tiles and labeled according to transcriptome-derived CMS classifications. We evaluated two architectures: a ResNet-34 convolutional neural network and a Vision Transformer (ViT-B-16), both trained on 128,181 tiles across both cohorts. Both models achieved comparable classification performance; however, ResNet-34 required significantly fewer computational resources. The ResNet-34 model was evaluated using 10-fold cross-validation and an independent test set, achieving an accuracy of ∼92 on the test set, with micro and macro average AUC values reaching ∼0.99. Despite the inherent class imbalance in CRC datasets, the model demonstrated strong generalization across all subtypes, highlighting the potential of image-based classification for CRC molecular subtyping. By enabling subtype identification from widely available histopathology slides, this method may support broader integration of molecular subtyping into precision oncology workflows. © 2026 The Authors.