DPSeq：一种基于Sequencer架构的新型高效数字病理分类器，用于预测癌症生物标志物
DPSeq: A Novel and Efficient Digital Pathology Classifier for Predicting Cancer Biomarkers using Sequencer Architecture
AI总结：
本研究提出基于sequencer架构的高效数字病理分类器DPSeq，在结直肠癌生物标志物预测任务中，性能优于多种CNN和transformer模型，且训练与预测速度更快。
摘要：
在数字病理任务中，transformer模型已取得超越卷积神经网络（CNNs）的最优性能，但transformer通常结构复杂且资源消耗高。本研究开发了一种名为DPSeq的新型高效数字病理分类器，通过微调整合了水平与垂直双向长短期记忆（BiLSTM）网络的sequencer架构，实现癌症生物标志物的预测。研究使用来自两个国际数据集——癌症基因组图谱（The Cancer Genome Atlas, TCGA）和分子与细胞肿瘤学（Molecular and Cellular Oncology, MCO）的结直肠癌（colorectal cancer, CRC）苏木精-伊红（H&E）染色组织病理图像，通过一系列实验评估DPSeq的预测性能。DPSeq在预测CRC关键生物标志物（MSI状态、超突变、CIMP状态、BRAF突变、TP53突变及染色体不稳定性[CING]）方面表现优异，在队列内部验证和跨队列外部验证中均优于多数已发表的最优分类器。此外，在相同实验条件、使用相同训练与测试数据集的情况下，DPSeq的表现优于4种CNN模型（ResNet18、ResNet50、MobileNetV2、EfficientNet）和2种transformer模型（ViT、Swin-T），在预测MSI状态、BRAF突变和CIMP状态时取得了最高的AUROC和AUPRC值。不仅如此，DPSeq架构简洁，训练和预测所需时间更短。因此，在癌症生物标志物预测任务中，DPSeq相比transformer和CNN模型是更优的选择。
英文摘要：
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.
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