基于残差网络的肺癌肿瘤突变负荷多分类预测模型

Multi-classification prediction model of lung cancer tumor mutation burden based on residualnetwork

经医学研究发现,肿瘤突变负荷(TMB)与非小细胞肺癌(NSCLC)免疫治疗的疗效呈正相关,并且TMB值对靶向治疗和化疗的疗效也有一定的预测作用。然而,计算TMB值需要借助全外显子组测序(WES)技术,成本较高。对此,本文利用临床常用的数字病理组织切片图像,研究TMB与切片图像之间的关联关系,并据此预测患者的TMB水平。本文提出了一种基于残差坐标注意力(RCA)结构并融合多尺度注意力引导(MSAG)模块的深度学习模型(RCA-MSAG)。该模型以50层残差网络(ResNet-50)为基准模型,并将坐标注意力(CA)融入到瓶颈(bottleneck)模块,用来捕获方向感知和位置敏感信息,从而使模型能够更准确定位和识别感兴趣的位置。然后,通过在网络内添加MSAG模块,使模型可以提取肺癌病理组织切片的深层特征以及通道之间的交互信息。本文实验数据集采用癌症基因组图谱(TCGA)公开数据集,数据集由200张肺腺癌病理组织切片组成,其中高TMB值的数据80张,中TMB值的数据77张,低TMB值的数据43张。实验结果表明,所提模型的准确率、精确率、召回率和F1分数分别为96.2%、96.4%、96.2%和96.3%,并且上述指标均优于当前主流深度学习模型。本文所提模型或可促进临床辅助诊断,对TMB预测具有一定的理论指导意义。

Medical studies have found that tumor mutation burden(TMB)is positively correlated with the efficacy of immunotherapy for non-small cell lung cancer(NSCLC),and TMB value can be used to predict the efficacy of targeted therapy and chemotherapy.However,the calculation of TMB value mainly depends on the whole exon sequencing(WES)technology,which usually costs too much time and expenses.To deal with above problem,this paper studies the correlation between TMB and slice images by taking advantage of digital pathological slices commonly used in clinic and then predicts the patient TMB level accordingly.This paper proposes a deep learning model(RCA-MSAG)based on residual coordinate attention(RCA)structure and combined with multi-scale attention guidance(MSAG)module.The model takes ResNet-50 as the basic model and integrates coordinate attention(CA)into bottleneck module to capture the direction-aware and position-sensitive information,which makes the model able to locate and identify the interesting positions more accurately.And then,MSAG module is embedded into the network,which makes the model able to extract the deep features of lung cancer pathological sections and the interactive information between channels.The cancer genome map(TCGA)open dataset is adopted in the experiment,which consists of 200 pathological sections of lung adenocarcinoma,including 80 data samples with high TMB value,77 data samples with medium TMB value and 43 data samples with low TMB value.Experimental results demonstrate that the accuracy,precision,recall and F1 score of the proposed model are 96.2%,96.4%,96.2%and 96.3%,respectively,which are superior to the existing mainstream deep learning models.The model proposed in this paper can promote clinical auxiliary diagnosis and has certain theoretical guiding significance for TMB prediction.