摘要
肿瘤突变负荷与非小细胞肺癌的免疫治疗疗效呈正相关,在临床实践中一般通过全外显子组测序来测量肿瘤突变负荷。然而,全外显子组测序操作复杂耗时、价格昂贵,导致大多数医院无法使用。基于此,提出了一种成本低、周期短、准确率高的基于病理组织切片预测肺腺癌肿瘤突变负荷的深度学习模型DBFormer。首先,颜色反卷积结构将输入模型的数字病理图像的RGB和HED图像信息相结合,丰富输入的病理图像信息,使模型更加适合医学任务分类;其次,图像通过四层金字塔结构,每层都包括一个最大池化层和一个DBFormer块,最大池化层减小图像尺寸、提升特征矩阵维度,DBFormer块包含归一化层和双重路由注意力机制对图像进行特征提取和处理;最后,从公开数据集TCGA-LUAD中随机选取337张和200张肺癌组织病理图像,分别构建二分类和三分类数据集进行实验。在二分类数据集上DBFormer模型的AUC,F1-Score,Precision,Recall,分别达到了99.7%,97.3%,97.6%,97.2%;在三分类数据集上DBFormer的Accuracy,Precision,Recall,F1-Score分别达到了97.3%,97.0%,97.0%,97.1%。实验结果表明,DBFormer模型相较于经典深度学习模型,在基于数字病理图像预测肺腺癌肿瘤突变负荷任务上具有更加优异的性能。
Tumor mutational burden(TMB)is positively correlated with the immunotherapy efficacy of non-small cell lung cancer(NSCLC).In clinical practice,tumor mutational burden is generally measured through whole exome sequencing(WES).How-ever,whole exome sequencing is complex,time-consuming,and expensive,making it inaccessible for most hospitals.In light of this,a low-cost,short-cycle,and high-accuracy deep learning model called DBFormer has been proposed to predict the tumor muta-tional burden of lung adenocarcinoma based on pathological tissue slices.Firstly,the color deconvolution structure combines the RGB and HED image information of the digital pathological images input into the model,enriching the information in the pathological images and making the model more suitable for medical task classification.Secondly,the images are processed through a four-layer pyramid structure,each layer consisting of a max-pooling layer and a DBFormer block.The max-pooling layer reduces the image size and increases the feature matrix dimensions,while the DBFormer block includes normalization layers and dual-route attention mechanisms for feature extraction and processing.Finally,337 and 200 lung cancer tissue pathological images are randomly selected from the TCGA-LUAD public dataset to construct binary and ternary classification datasets for experimentation.On the binary classification dataset,the DBFormer model achieves AUC,F1-Score,Precision,and Recall of 99.7%,97.3%,97.6%,and 97.2%,respectively.On the ternary classification dataset,DBFormer achieves an Accuracy,Precision,Recall,and F1-Score of 97.3%,97.0%,97.0%,and 97.1%,respectively.Experimental results demonstrate that the DBFormer model outperforms classical deep learning models in predicting the tumor mutational burden of lung adenocarcinoma based on di-gital pathological images.
作者
关昕
杨雪永
杨啸林
孟祥福
GUAN Xin;YANG Xueyong;YANG Xiaolin;MENG Xiangfu(School of Electronic and Information Engineering,Liaoning Technical University,Huludao,Liaoning 125105,China;Institute of Basic Medicine,Chinese Academy of Medical Sciences,School of Basic Medicine,Peking Union Medical College,Beijing 100005,China)
出处
《计算机科学》
北大核心
2025年第S1期91-98,共8页
Computer Science
基金
国家自然科学基金(61772249)
辽宁省教育厅一般项目(LJ2019QL017,LJKZ0355)。
关键词
肿瘤突变负荷预测
肺腺癌
组织病理图像
深度学习模型
自注意力机制
Tumor mutation burden prediction
Lung adenocarcinoma
Histopathological image
Deep learning model
Self-attention mechanism
