RcaNet:一种预测肿瘤突变负荷的深度学习模型

doi:10.3969/j.issn.0258-8021.2023.01.006

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (6906 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract In recent years, the morbidity and mortality of lung cancer have been rising continuously, and it has become one of the most dangerous malignant tumors that threaten human life and health. The incidence of non-small cell lung cancer (NSCLC) accounts for more than 80% of the total incidence of lung cancer. Due to its complicated diagnostic process and high diagnostic cost, the effective diagnosis and treatment of NSCLC have become a great challenge for doctors. It has found that tumor mutation burden (TMB) is positively correlated with the efficacy of NSCLC immunotherapy, and TMB value has a certain predictive effect on the efficacy of targeted therapy and chemotherapy. Based on the above findings, a deep learning model (RcaNet) was proposed. In this model, a residual network (ResNet) was taken as the backbone network, and multi-dimensional feature attention and multi-scale information fusion were added in the network, enhancing the ability of the network in paying attention to and extract the deep features of lung cancer pathological sections. Experiments were performed with RcaNet and the mainstream deep learning models on the TCGA public data set with experimental training samples of 925 954. The results showed that the average area under the curve (AUC) of the RcaNet model is 0.883 0, which is 6.8% higher than that of CAIM model, 4.2% higher than that of ResNeSt model, and 5.3% higher than that of ResNet model. Our proposed method has guiding significance and application value for the diagnosis and treatment of NSCLC.

Key words： non-small cell lung cancer attention to multidimensional features tumor mutation burden deep learning

Received: 27 April 2022

PACS:

R318

Corresponding Authors: *E-mail:yangxl@pumc.edu.cn;marxi@126.com

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Liu Deng
	Yang Xiaolin
	Meng Xiangfu

Cite this article:

Liu Deng,Yang Xiaolin,Meng Xiangfu. RcaNet: A Deep Learning Model for Predicting Tumor Mutation Burden[J]. Chinese Journal of Biomedical Engineering, 2023, 42(1): 51-61.

URL:

http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2023.01.006 OR http://cjbme.csbme.org/EN/Y2023/V42/I1/51

[1] Pang Shanchen, Zhang Yaqin, Ding Mao, et al. A deep model for lung cancer type identification by densely connected convolutional networks and adaptive boosting [J]. IEEE Access, 2019, 8(8): 4799-4805.
[2] Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries [J]. CA Cancer J Clin, 2021, 71(3):209-249.
[3] Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries [J]. CA Cancer J Clin, 2020, 70(4): 313-313.
[4] Rizvi NA, Hellmann MD, Snyder A, et al. Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer [J]. Science, 2015, 348(6230): 124-128.
[5] Goodman AM, Kato S, Bazhenova L, et al. Tumor mutational burden as an independent predictor of response to immunotherapy in diverse cancers[J]. Molecular Cancer Therapeutics, 2017, 16(11): 2598-2608.
[6] Chalmers ZR, Connelly CF, Fabrizio D, et al. Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden [J]. Genome Medicine, 2017, 9(1): 1-14.
[7] Wei JW, Tafe LJ, Linnik YA, et al. Pathologist-level classifification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks [J]. Sci Rep, 2019, 9 (1):3358-3358.
[8] 孙德伟,王志刚,杨啸林,等.基于深度学习的肺腺癌肿瘤突变负荷的预测[J].中国生物医学工程学报,2021,40(6):681-690.
[9] Coudray N, Ocampo PS, Sakellaropoulos T, et al. Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning [J]. Nature Medicine, 2018, 24(10): 1559-1567.
[10] He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Deep residual learning for image recognition [C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 770-778.
[11] Razavian Ali S, Azizpour H, Sullivan J, et al. CNN features off-the-shelf: an astounding baseline for recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. Columbus: IEEE, 2014:806-813.
[12] Benzheng Wei,Han Zhongyi,Xueying He,et al. Deep learning model based breast cancer histopathological image classification[C]//IEEE International Conference on Cloud Computing & Big Data Analysis. Chengdu: IEEE, 2017:348-353.
[13] Faust K, Xie Q, Han D, et al. Visualizing histopathologic deep learning classifification and anomaly detection using nonlinear feature space dimensionality reduction[J]. BMC Bioinformatics, 2018. 19 (1), 173.
[14] Stenzinger A, Allen JD, Maas J, et al. Tumor mutational burden standardization initiatives: recommendations for consistent tumor mutational burden assessment in clinical samples to guide immunotherapy treatment decisions [J]. Genes, Chromosomes and Cancer, 2019, 58(8): 578-588.
[15] 江昌,易玲,高翔,等.非小细胞肺癌免疫治疗生物标志物研究进展[J].中国肿瘤杂志,2022,25(1):46-53.
[16] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[C] //The 3rd Intemational Conference on Learning Representations. San Diego: IEEE, 2014:1-14.
[17] Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 2818-2826.
[18] Szegedy C, Ioffe S, Vanhoucke V, et al. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning[C]// Proceedings of the Thirty-First AAAI Conference on artificial Intelligence. San Francisco: AAAI, 2016: 4278-4284.
[19] Zhang Hang, Wu Chongruo, Zhang Zhongyue, et al. ResNeSt: Split-Attention Networks [C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020:2736-2746.
[20] Tan Mingxing, Le QV. EfficientNet: rethinking model scaling for convolutional neural networks [C]//International Conference on Machine Learning. Long Beach: ICML, 2019:6105-6114.
[21] Steuer CE, Ramalingam SS. Tumor mutation burden: leading immunotherapy to the era of precision medicine [J]. J Clin Oncol, 2018, 36(7) : 631-632.
[22] Galon J, Bruni D. Approaches to treat immune hot, altered and cold tumours with combination immunotherapies [J]. Nature Reviews Drug Discovery, 2019, 18(3): 197-218.
[23] Bergstra J, Bardenet R, Bengio Y, et al. Algorithms for hyperparameter optimization [J]. Advances in Neural Information Processing Systems, 2021, 24: 2546-2566.
[24] Chen Cheng, Dou Qi, Chen Hao, et al. Synergistic image and feature adaptation:towands cross-modality domain adaptation for medical image segmentation [C] //Proceedings of the AAAI Conference on Artificial Intelligence. Hawaii: AAAI, 2019: 865-872.