Deep CNN Model Using CT Radiomics Feature Mapping Recognizes EGFR Gene Mutation Status of Lung Adenocarcinoma.
Deep CNN Model Using CT Radiomics Feature Mapping Recognizes EGFR Gene Mutation Status of Lung Adenocarcinoma.
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DOI：
10.3389/fonc.2020.598721
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发表时间：
2020
影响因子：
4.7
通讯作者：
Guan Y
中科院分区：
文献类型：
--
作者：
Zhang B;Qi S;Pan X;Li C;Yao Y;Qian W;Guan Y
识别肺腺癌（LADC）中表皮生长因子受体（EGFR）基因突变状态已成为决定EGFR-酪氨酸激酶抑制剂（EGFR- tki）药物是否可以使用的前提。聚合酶链反应测定或基因测序是用于测量EGFR状态，然而，需要通过手术或活检获得组织样本。我们建议开发深度学习模型，利用从非侵入性CT图像中提取的放射组学特征来识别EGFR状态。收集了709例患者的队列（主要队列）和205例患者的独立队列的术前CT图像、EGFR突变状态和临床资料。在每个病变区域提取1037个基于ct的放射组学特征后，选择784个判别性特征进行分析，构建特征映射。设计并训练了一个压缩-激励（SE）卷积神经网络（SE- cnn），用于从放射组学特征映射中识别EGFR状态。SE-CNN模型通过638例初始队列患者进行训练和验证，使用其余71例患者（内部测试队列）进行测试，并使用独立的205例患者（外部测试队列）进行进一步测试。此外，SE-CNN模型使用放射组学特征、临床特征和两者都使用的特征与机器学习（ML）模型进行比较。与EGFR（+）相比，EGFR（-）患者年龄更小，女性患病几率更高，病变体积更大，发生腺泡显性腺癌（APA）亚型的几率更低。最具判别性的特征是纹理（614,78.3%），其次是一阶强度特征（158,20.1%）和形状特征（12,1.5%）。SE-CNN模型可以识别EGFR突变状态，内部和外部测试队列的AUC分别为0.910和0.841。它比没有SE的CNN模型、经过微调的VGG16和VGG19模型、三种ML模型和最先进的模型都要好。SE-CNN利用从无创CT图像中提取的放射组学特征映射，可以准确识别LADC患者的EGFR突变状态。结合放射组学特征和深度学习的方法优于ML方法，可以扩展到其他医学应用。SE-CNN模型有助于EGFR-TKI药物的使用决策。
To recognize the epidermal growth factor receptor (EGFR) gene mutation status in lung adenocarcinoma (LADC) has become a prerequisite of deciding whether EGFR-tyrosine kinase inhibitor (EGFR-TKI) medicine can be used. Polymerase chain reaction assay or gene sequencing is for measuring EGFR status, however, the tissue samples by surgery or biopsy are required. We propose to develop deep learning models to recognize EGFR status by using radiomics features extracted from non-invasive CT images. Preoperative CT images, EGFR mutation status and clinical data have been collected in a cohort of 709 patients (the primary cohort) and an independent cohort of 205 patients. After 1,037 CT-based radiomics features are extracted from each lesion region, 784 discriminative features are selected for analysis and construct a feature mapping. One Squeeze-and-Excitation (SE) Convolutional Neural Network (SE-CNN) has been designed and trained to recognize EGFR status from the radiomics feature mapping. SE-CNN model is trained and validated by using 638 patients from the primary cohort, tested by using the rest 71 patients (the internal test cohort), and further tested by using the independent 205 patients (the external test cohort). Furthermore, SE-CNN model is compared with machine learning (ML) models using radiomics features, clinical features, and both features. EGFR(-) patients show the smaller age, higher odds of female, larger lesion volumes, and lower odds of subtype of acinar predominant adenocarcinoma (APA), compared with EGFR(+). The most discriminative features are for texture (614, 78.3%) and the features of first order of intensity (158, 20.1%) and the shape features (12, 1.5%) follow. SE-CNN model can recognize EGFR mutation status with an AUC of 0.910 and 0.841 for the internal and external test cohorts, respectively. It outperforms the CNN model without SE, the fine-tuned VGG16 and VGG19, three ML models, and the state-of-art models. Utilizing radiomics feature mapping extracted from non-invasive CT images, SE-CNN can precisely recognize EGFR mutation status of LADC patients. The proposed method combining radiomics features and deep leaning is superior to ML methods and can be expanded to other medical applications. The proposed SE-CNN model may help make decision on usage of EGFR-TKI medicine.
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