基于深度学习的肺腺癌肿瘤突变负荷的预测

doi:10.3969/j.issn.0258-8021.2021.06.05

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Abstract A number of existing medical studies have found out that tumor mutation burden (TMB) is positively correlated with the efficacy of non-small cell lung cancer (NSCLC) immunotherapy, and in the recent related studies, tumor mutation load also has a certain predictive effect on the efficacy of targeted therapy and chemotherapy. Based on above situations, this paper proposed an inception deep learning model CAIM (combine attention and inception-block module) to identify the pathological sections of lung adenocarcinoma in non-small cell lung cancer from the Cancer Genome Atlas (TCGA) dataset. First, by segmenting the data samples, cutting them into small slices, and then sending them to the deep learning model, learning image features through convolution, and then combining with the attention mechanism to further strengthen the feature extraction. Finally, through the integration of the prediction information of the small slices, the TMB value of the pathological tiles of lung adenocarcinoma (LUAD) could be automatically determined. The data set consisted of 337 LUAD pathological tissue sections, including 271 data with high TMB value and 66 data experiments with low TMB value. Experimental results showed that the averaged area under the curve (AUC) of the proposed method was 0.82, which significantly better than the AUC value of 0.66 for the residual network of image classification method and was of great significance for the detection of tumor mutation burden and auxiliary diagnosis in clinical practice.

Key words： lung adenocarcinoma tumor mutation burden deep learning

Received: 13 May 2021

PACS:

R318

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	Articles by authors
	Sun Dewei
	Wang Zhigang
	Yang Xiaolin
	Meng Xiangfu

Cite this article:

Sun Dewei,Wang Zhigang,Yang Xiaolin, et al. Prediction Tumor Mutation Burden of Lung Adenocarcinoma Based on Deep Learning[J]. Chinese Journal of Biomedical Engineering, 2021, 40(6): 681-690.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2021.06.05 OR http://cjbme.csbme.org/EN/Y2021/V40/I6/681

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