Prediction of Target-Drug Therapy by Identifying Gene Mutations in Lung Cancer With Histopathological Stained Image and Deep Learning Techniques.
Prediction of Target-Drug Therapy by Identifying Gene Mutations in Lung Cancer With Histopathological Stained Image and Deep Learning Techniques.
复制标题
利用组织病理学染色图像和深度学习技术识别肺癌基因突变来预测靶向药物治疗
DOI：
10.3389/fonc.2021.642945
复制
发表时间：
2021
影响因子：
4.7
通讯作者：
Wu FX
中科院分区：
文献类型：
--
作者：
Huang K;Mo Z;Zhu W;Liao B;Yang Y;Wu FX
肺癌是一种发病率和死亡率都很高的癌症，与多种基因突变有关。个体化靶向药物治疗已成为肺癌的最佳治疗方法，特别是对不具备肺叶切除术资格的患者更是受益。从染色的病理切片中准确识别肿瘤区域内的突变基因至关重要。因此，我们主要致力于通过对病理图像的分析来识别肺癌的突变基因。在这项研究中，我们提出了一种通过组织病理染色图像和深度学习来识别肺癌基因突变来预测靶向药物治疗的方法，称为DeepIMLH。DeepIMLH算法首先从癌症基因图谱(TCGA)下载了180张肺癌苏木精-伊红染色(H&E)图像。然后利用深度卷积高斯混合模型(DCGMM)进行颜色归一化。将卷积神经网络(CNN)和残差网络(RES-net)用于H&E染色图像中突变基因的识别，取得了较好的准确率。这表明我们的方法可以用于选择靶向药物治疗，并可能应用于临床实践。不过，还需要进行更多的研究。
Lung cancer is a kind of cancer with high morbidity and mortality which is associated with various gene mutations. Individualized targeted-drug therapy has become the optimized treatment of lung cancer, especially benefit for patients who are not qualified for lung lobectomy. It is crucial to accurately identify mutant genes within tumor region from stained pathological slice. Therefore, we mainly focus on identifying mutant gene of lung cancer by analyzing the pathological images. In this study, we have proposed a method by identifying gene mutations in lung cancer with histopathological stained image and deep learning to predict target-drug therapy, referred to as DeepIMLH. The DeepIMLH algorithm first downloaded 180 hematoxylin-eosin staining (H&E) images of lung cancer from the Cancer Gene Atlas (TCGA). Then deep convolution Gaussian mixture model (DCGMM) was used to perform color normalization. Convolutional neural network (CNN) and residual network (Res-Net) were used to identifying mutated gene from H&E stained imaging and achieved good accuracy. It demonstrated that our method can be used to choose targeted-drug therapy which might be applied to clinical practice. More studies should be conducted though.
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DOI：
10.1016/j.jtho.2016.04.033
发表时间：
2016-08
期刊：
Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer
影响因子：
--
作者：
Noonan SA;Berry L;Lu X;Gao D;Barón AE;Chesnut P;Sheren J;Aisner DL;Merrick D;Doebele RC;Varella-Garcia M;Camidge DR
通讯作者：
Camidge DR
影响因子：
11.1
作者：
Khosravi P;Kazemi E;Imielinski M;Elemento O;Hajirasouliha I
通讯作者：
Hajirasouliha I
影响因子：
6.2
作者：
Raab, SS;Grzybicki, DM;Geyer, SJ
通讯作者：
Geyer, SJ
影响因子：
45.3
作者：
Lai, Gillianne Gy;Lim, Tse Hui;Tan, Daniel S. W.
通讯作者：
Tan, Daniel S. W.
影响因子：
9.6
作者：
Franklin, WA
通讯作者：
Franklin, WA