DPSeq: A Novel and Efficient Digital Pathology Classifier for Predicting Cancer Biomarkers using Sequencer Architecture

Abstract

In digital pathology tasks, transformers have achieved state-of-the-art results, surpassing convolutional neural networks (CNNs). However, transformers are usually complex and resource intensive. In this study, we developed a novel and efficient digital pathology classifier called DPSeq, to predict cancer biomarkers through fine-tuning a sequencer architecture integrating horizon and vertical bidirectional long short-term memory (BiLSTM) networks. Using hematoxylin and eosin (H&E)-stained histopathological images of colorectal cancer (CRC) from two international datasets: The Cancer Genome Atlas (TCGA) and Molecular and Cellular Oncology (MCO), the predictive performance of DPSeq was evaluated in series of experiments. DPSeq demonstrated exceptional performance for predicting key biomarkers in CRC (MSI status, Hypermutation, CIMP status, BRAF mutation, TP53 mutation and chromosomal instability [CING]), outperforming most published state-of-the-art classifiers in a within-cohort internal validation and a cross-cohort external validation. Additionally, under the same experimental conditions using the same set of training and testing datasets, DPSeq surpassed 4 CNN (ResNet18, ResNet50, MobileNetV2, and EfficientNet) and 2 transformer (ViT and Swin-T) models, achieving the highest AUROC and AUPRC values in predicting MSI status, BRAF mutation, and CIMP status. Furthermore, DPSeq required less time for both training and prediction due to its simple architecture. Therefore, DPSeq appears to be the preferred choice over transformer and CNN models for predicting cancer biomarkers.