Huang Kaimei, Mo Zhiyi, Zhu Wen, Liao Bo, Yang Yachao, Wu Fang-Xiang
Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.
Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China.
Front Oncol. 2021 Apr 13;11:642945. doi: 10.3389/fonc.2021.642945. eCollection 2021.
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.
肺癌是一种发病率和死亡率都很高的癌症，它与多种基因突变有关。个体化靶向药物治疗已成为肺癌的优化治疗方法，尤其对那些不适合进行肺叶切除术的患者有益。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分析病理图像来识别肺癌的突变基因。在本研究中，我们提出了一种利用组织病理学染色图像和深度学习来识别肺癌基因突变以预测靶向药物治疗的方法，称为DeepIMLH。DeepIMLH算法首先从癌症基因组图谱（TCGA）下载了180张肺癌苏木精-伊红染色（H&E）图像。然后使用深度卷积高斯混合模型（DCGMM）进行颜色归一化。利用卷积神经网络（CNN）和残差网络（Res-Net）从H&E染色图像中识别突变基因，并取得了良好的准确率。结果表明，我们的方法可用于选择可能应用于临床实践的靶向药物治疗。不过，还需要进行更多的研究。
