摘要
肺癌是一种常见的肺部恶性肿瘤,是全球发病率和死亡率最高的恶性肿瘤。对于发生了表皮生长因子受体(EGFR)基因突变的晚期非小细胞型肺癌患者,可以使用靶向药物来进行针对性治疗。EGFR基因突变的检测方法很多,但是各有优缺点。本文拟通过探索非小细胞型肺癌苏木精-伊红(HE)染色的全扫描组织病理图像形态学特征与患者EGFR基因突变之间的关联,达到预测EGFR基因突变风险的目的。实验结果表明,本文所提出的EGFR基因突变风险预测模型的曲线下面积(AUC)在测试集上可达72.4%,准确率为70.8%,提示非小细胞型肺癌全扫描组织病理图像中的组织形态学特征与EGFR基因突变之间存在密切关联。本文从病理图像的尺度来分析基因分子表型,将病理组学和分子组学相融合,建立EGFR基因突变风险预测模型,揭示全扫描组织病理图像和EGFR基因突变风险的关联性,或可为该领域提供一个颇具前景的研究方向。
Lung cancer is a most common malignant tumor of the lung and is the cancer with the highest morbidity and mortality worldwide.For patients with advanced non-small cell lung cancer who have undergone epidermal growth factor receptor(EGFR)gene mutations,targeted drugs can be used for targeted therapy.There are many methods for detecting EGFR gene mutations,but each method has its own advantages and disadvantages.This study aims to predict the risk of EGFR gene mutation by exploring the association between the histological features of the whole slides pathology of non-small cell lung cancer hematoxylin-eosin(HE)staining and the patient's EGFR mutant gene.The experimental results show that the area under the curve(AUC)of the EGFR gene mutation risk prediction model proposed in this paper reached 72.4%on the test set,and the accuracy rate was 70.8%,which reveals the close relationship between histomorphological features and EGFR gene mutations in the whole slides pathological images of non-small cell lung cancer.In this paper,the molecular phenotypes were analyzed from the scale of the whole slides pathological images,and the combination of pathology and molecular omics was used to establish the EGFR gene mutation risk prediction model,revealing the correlation between the whole slides pathological images and EGFR gene mutation risk.It could provide a promising research direction for this field.
作者
王荃
沈勤
张泽林
蔡程飞
鲁浩达
周晓军
徐军
WANG Quan;SHEN Qin;ZHANG Zelin;CAI Chengfei;LU Haoda;ZHOU Xiaojun;XU Jun(School of Automation,Nanjing University of Information Science and Technology,Nanjing 210044,P.R.China;Jiangsu Key Laboratory of Large Data Analysis Technology,Nanjing 210044,P.R.China;Department of Pathology,Nanjing General Hospital,Nanjing 210002,P.R.China)
出处
《生物医学工程学杂志》
EI
CAS
CSCD
北大核心
2020年第1期10-18,共9页
Journal of Biomedical Engineering
基金
国家自然科学基金（U1809205,61771249,81871352）
江苏省自然科学基金（BK20181411）
江苏省“青蓝工程”资助
关键词
深度学习
肺癌
病理图像
基因突变
精准医疗
deep learning
lung cancer
histopathological image
gene mutation
precision medicine
