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.

本研究开发了一种高效的数字病理分类器 DPSeq，通过微调附带水平和垂直双向长短期记忆 (BiLSTM) 网络的测序器结构，预测结肠直肠癌 (CRC) 的癌症生物标志物，在国际数据集 MCO 和 TCGA 上评估了 DPSeq 的预测性能表现优异，并在预测 MSI 状态、BRAF 突变和 CIMP 状态方面超过了 4 个 CNN 和 2 个 transformer 模型。