Multi-classification prediction model of lung cancer tumor mutation burden based on residual network_Journal of Biomedical Engineering

Authors：

 MENG Xiangfu ¹ , YU Chunlin ¹ , YANG Xiaolin ² , YANG Ziyi ¹ , LIU Deng ¹

1. School of Electronics and Information Engineering, Liaoning Technical University, Huludao, Liaoning 125000, P. R. China;
2. Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences/ School of Basic Medicine, Peking Union Medical College, Beijing 100005, P. R. China;

Corresponding author：

MENG Xiangfu, Email: marxi@126.com

Keywords：

Non-small cell lung cancer; Tumor mutation burden; Residual network; Multi-scale attention

DOI：

10.7507/1001-5515.202304055

Video：

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Medical studies have found that tumor mutation burden (TMB) is positively correlated with the efficacy of immunotherapy for non-small cell lung cancer (NSCLC), and TMB value can be used to predict the efficacy of targeted therapy and chemotherapy. However, the calculation of TMB value mainly depends on the whole exon sequencing (WES) technology, which usually costs too much time and expenses. To deal with above problem, this paper studies the correlation between TMB and slice images by taking advantage of digital pathological slices commonly used in clinic and then predicts the patient TMB level accordingly. This paper proposes a deep learning model (RCA-MSAG) based on residual coordinate attention (RCA) structure and combined with multi-scale attention guidance (MSAG) module. The model takes ResNet-50 as the basic model and integrates coordinate attention (CA) into bottleneck module to capture the direction-aware and position-sensitive information, which makes the model able to locate and identify the interesting positions more accurately. And then, MSAG module is embedded into the network, which makes the model able to extract the deep features of lung cancer pathological sections and the interactive information between channels. The cancer genome map (TCGA) open dataset is adopted in the experiment, which consists of 200 pathological sections of lung adenocarcinoma, including 80 data samples with high TMB value, 77 data samples with medium TMB value and 43 data samples with low TMB value. Experimental results demonstrate that the accuracy, precision, recall and F1 score of the proposed model are 96.2%, 96.4%, 96.2% and 96.3%, respectively, which are superior to the existing mainstream deep learning models. The model proposed in this paper can promote clinical auxiliary diagnosis and has certain theoretical guiding significance for TMB prediction.

Citation： MENG Xiangfu, YU Chunlin, YANG Xiaolin, YANG Ziyi, LIU Deng. Multi-classification prediction model of lung cancer tumor mutation burden based on residual network. Journal of Biomedical Engineering, 2023, 40(5): 867-875. doi: 10.7507/1001-5515.202304055 Copy

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21.	Krizhevsky A, Sutskever I, Hinton G. ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 2012, 25(2): 1097-1105.
22.	Szegedy C, Ioffe S, Vanhoucke V, et al. Inception-v4, Inception-ResNet and the impact of residual connections on learning//Proceedings of the AAAI Conference on artificial Intelligence, AAAI, 2016: 4278-4284.
23.	Tan M, Le Q. Efficientnet: rethinking model scaling for convolutional neural networks//The 36^th International Conference on Machine Learning. ArXiv, 2019. abs/1905.11946.
24.	Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 2261-2269.
25.	Zhang Hang, Wu Chongruo, Zhang Zhongyue, et al. ResNeSt: split-attention networks//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), New Orleans: IEEE, 2022: 2735-2745.

1. Saranya N, Kanthimathi N, Boomika S, et al. Classification and prediction of lung cancer with histopathological images using VGG-19 architecture. IFIP Advances in Information and Communication Technology (IFIPAICT), 2022, 654: 152-161.
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. CA Cancer J Clin, 2021, 71(3): 209-249.
3. Xu Z, Ren H, Zhou W, et al. ISANET: non-small cell lung cancer classification and detection based on CNN and attention mechanism. Biomedical Signal Processing and Control, 2022, 77: 103773.
4. Rizvi N A, Hellmann M D, Snyder A, et al. Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer. Science, 2015, 348(6230): 124-128.
5. Greillier L, Tomasini P, Barlesi F. The clinical utility of tumor mutational burden in non-small cell lung cancer. Transl Lung Cancer Res, 2018, 7(6): 639-646.
6. Coudray N, Ocampo P S, Sakellaropoulos T, et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med, 2018, 24(10): 1559-1567.
7. Christopher T, Banu J J. Study of classification algorithm for lung cancer prediction. International Journal of Innovative Science, Engineering & Technology, 2016, 3(2): 42-49.
8. Deniz E, Şengür A, Kadiroğlu Z, et al. Transfer learning based histopathologic image classification for breast cancer detection. Health Inf Sci Syst, 2018, 6(1): 18.
9. 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. 中国生物医学工程学报, 2021, 40(6): 681-690.
10. 刘邓, 杨啸林, 孟祥福. RcaNet: 一种预测肿瘤突变负荷的深度学习模型. 中国生物医学工程学报, 2023, 42(1): 51-61.
11. Stenzinger A, Allen J D, Maas J, et al. Tumor mutational burden standardization initiatives: recommendations for consistent tumor mutational burden assessment in clinical samples to guide immunotherapy treatment decisions. Genes, Chromosomes and Cancer, 2019, 58(8): 578-588.
12. Hou Q, Zhou D, Feng J. Coordinate attention for efficient mobile network design//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2021: 13713-13722.
13. He K, Zhang X, Ren S, et al. Deep residual learning for image recognition// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2016: 770-778.
14. Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift// International Conference on Machine Learning, 2015. Corpus ID: 5808102.
15. Glorot X, Bordes A, Bengio Y. Deep sparse rectifier neural networks. Journal of Machine Learning Research, 2011, 15: 315-323.
16. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition// Proceedings of the International Conference on Learning Representations, 2015. arXiv: 1409.1556v6.
17. Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision// IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2016: 2818-2826.
18. Hu J, Shen L, Sun G. Squeeze-and-excitation networks// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018: 7132-7141.
19. Woo S, Park J, Lee J Y, et al. CBAM: convolutional block attention module//Proceedings of the European Conference on Computer Vision, Springer, 2018: 3-19.
20. Howard A G, Zhu M, Chen B, et al. Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint, 2017: 1704.04861.
21. Krizhevsky A, Sutskever I, Hinton G. ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 2012, 25(2): 1097-1105.
22. Szegedy C, Ioffe S, Vanhoucke V, et al. Inception-v4, Inception-ResNet and the impact of residual connections on learning//Proceedings of the AAAI Conference on artificial Intelligence, AAAI, 2016: 4278-4284.
23. Tan M, Le Q. Efficientnet: rethinking model scaling for convolutional neural networks//The 36^th International Conference on Machine Learning. ArXiv, 2019. abs/1905.11946.
24. Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 2261-2269.
25. Zhang Hang, Wu Chongruo, Zhang Zhongyue, et al. ResNeSt: split-attention networks//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), New Orleans: IEEE, 2022: 2735-2745.

Journal of Biomedical Engineering

Multi-classification prediction model of lung cancer tumor mutation burden based on residual network

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