Prediction of gene mutation in lung cancer based on deep learning and histomorphology analysis_Journal of Biomedical Engineering

Authors：

WANG Quan ^1,2 , SHEN Qin ³ , ZHANG Zelin ^1,2 , CAI Chengfei ^1,2 , LU Haoda ^1,2 , ZHOU Xiaojun ³ ,  XU Jun ^1,2

1. School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, P.R.China;
2. Jiangsu Key Laboratory of Large Data Analysis Technology, Nanjing 210044, P.R.China;
3. Department of Pathology, Nanjing General Hospital, Nanjing 210002, P.R.China;

Corresponding author：

XU Jun, Email: xujung@gmail.com

Keywords：

deep learning; lung cancer; histopathological image; gene mutation; precision medicine

DOI：

10.7507/1001-5515.201904018

Video：

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Lung cancer is a most common malignant tumor of the lung and is the cancer with the highest morbidity and mortality worldwide. For patients with advanced non-small cell lung cancer who have undergone epidermal growth factor receptor (EGFR) gene mutations, targeted drugs can be used for targeted therapy. There are many methods for detecting EGFR gene mutations, but each method has its own advantages and disadvantages. This study aims to predict the risk of EGFR gene mutation by exploring the association between the histological features of the whole slides pathology of non-small cell lung cancer hematoxylin-eosin (HE) staining and the patient's EGFR mutant gene. The experimental results show that the area under the curve (AUC) of the EGFR gene mutation risk prediction model proposed in this paper reached 72.4% on the test set, and the accuracy rate was 70.8%, which reveals the close relationship between histomorphological features and EGFR gene mutations in the whole slides pathological images of non-small cell lung cancer. In this paper, the molecular phenotypes were analyzed from the scale of the whole slides pathological images, and the combination of pathology and molecular omics was used to establish the EGFR gene mutation risk prediction model, revealing the correlation between the whole slides pathological images and EGFR gene mutation risk. It could provide a promising research direction for this field.

Citation： WANG Quan, SHEN Qin, ZHANG Zelin, CAI Chengfei, LU Haoda, ZHOU Xiaojun, XU Jun. Prediction of gene mutation in lung cancer based on deep learning and histomorphology analysis. Journal of Biomedical Engineering, 2020, 37(1): 10-18. doi: 10.7507/1001-5515.201904018 Copy

1.	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. CA Cancer J Clin, 2018, 68(6): 394-424.
2.	郑荣寿, 孙可欣, 张思维, 等. 2015年中国恶性肿瘤流行情况分析. 中华肿瘤杂志, 2019, 41(1): 19-28.
3.	Ganeshan B, Panayiotou E, Burnand K, et al. Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival. Eur Radiol, 2012, 22(4): 796-802.
4.	刘红雨, 李颖, 陈钢, 等. 187例非小细胞肺癌中EGFR基因突变和扩增的检测及其临床意义. 中国肺癌杂志, 2009, 4(12): 1219-1228.
5.	Chan B A, Hughes B G. Targeted therapy for non-small cell lung cancer: current standards and the promise of the future. Translational lung cancer research, 2015, 4(1): 36-54.
6.	Junttila M R, de Sauvage F J. Influence of tumour micro-environment heterogeneity on therapeutic response. Nature, 2013, 501(7467): 346-354.
7.	Mobadersany P, Yousefi S, Amgad M, et al. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci U S A, 2018, 115(13): E2970-E2979.
8.	Xu Jun, Luo Xiaofei, Wang Guanhao, et al. A deep convolutional neural network for segmenting and classifying epithelial and stromal regions in histopathological images. Neurocomputing, 2016, 191(3): 214-223.
9.	Yu K H, Zhang Ce, Berry G J, et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun, 2016, 7(5): 12474.
10.	Vaidya P, Wang X, Bera K, et al. RaPtomics: integrating radiomic and pathomic features for predicting recurrence in early stage lung cancer. Digital Pathology, 2018, 6(2): 105-118.
11.	Aerts H J, Grossmann P, Tan Yongqiang, et al. Defining a radiomic response phenotype: a pilot study using targeted therapy in NSCLC. Sci Rep, 2016, 6(2): 33860.
12.	Liu Ying, Kim J, Balagurunathan Y, et al. Radiomic features are associated with EGFR mutation status in lung adenocarcinomas. Clin Lung Cancer, 2016, 17(5): 441-448.
13.	Isola P, Zhu Junyan, Zhou Tinghui, et al. Image-to-image translation with conditional adversarial networks//30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), 2017: 5967-5976.
14.	Khan A M, Rajpoot N, Treanor D, et al. A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution. IEEE Trans Biomed Eng, 2014, 61(6): 1729-1738.
15.	Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation//Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer, 2015, 9351: 234-241.
16.	Ali S, Lewis J, Madabhushi A. Spatially aware cell cluster graphs: predicting outcome in oropharyngeal pl6+ tumors//International Conference on Medical Image Computing and Computer-Assisted Intervention, Berlin: Springer, 2013: 412-419.
17.	Haralick R M, Shanmugam K, Dinstein H. Textural features for image classification. IEEE Transactions on Systems, 1973, 3(6): 610-621.
18.	Duyckaerts C, Godefroy G. Voronoi tessellation to study the numerical density and the spatial distribution of neurones. J Chem Neuroanat, 2000, 20(1): 83-92.
19.	Lee G, Ali S, Veltri R, et al. Cell orientation entropy (COrE): predicting biochemical recurrence from prostate cancer tissue microarrays. Med Image Comput Comput Assist Interv, 2013, 16(3): 396-403.
20.	Peng Hanchuan, Long Fuhui, Ding C. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell, 2005, 27(8): 1226-1238.
21.	Suykens J, Vandewalle J. Least squares support vector machine classifiers. Neural Processing Letters, 1999, 9(3): 293-300.
22.	Furey T S, Cristianini N, Duffy N, et al. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics, 2000, 16(10): 906-914.
23.	Tong S, Koller D. Support vector machine active learning with applications to text classification. Journal of Machine Learning Research, 2002, 15(21): 999-1006.
24.	Ginsburg S B, Viswanath S E, Bloch B, et al. Novel PCA-VIP scheme for ranking MRI protocols and identifying computer-extracted MRI measurements associated with central gland and peripheral zone prostate tumors. Journal of Magnetic Resonance Imaging, 2015, 41(5): 1383-1393.

1. 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. CA Cancer J Clin, 2018, 68(6): 394-424.
2. 郑荣寿, 孙可欣, 张思维, 等. 2015年中国恶性肿瘤流行情况分析. 中华肿瘤杂志, 2019, 41(1): 19-28.
3. Ganeshan B, Panayiotou E, Burnand K, et al. Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival. Eur Radiol, 2012, 22(4): 796-802.
4. 刘红雨, 李颖, 陈钢, 等. 187例非小细胞肺癌中EGFR基因突变和扩增的检测及其临床意义. 中国肺癌杂志, 2009, 4(12): 1219-1228.
5. Chan B A, Hughes B G. Targeted therapy for non-small cell lung cancer: current standards and the promise of the future. Translational lung cancer research, 2015, 4(1): 36-54.
6. Junttila M R, de Sauvage F J. Influence of tumour micro-environment heterogeneity on therapeutic response. Nature, 2013, 501(7467): 346-354.
7. Mobadersany P, Yousefi S, Amgad M, et al. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci U S A, 2018, 115(13): E2970-E2979.
8. Xu Jun, Luo Xiaofei, Wang Guanhao, et al. A deep convolutional neural network for segmenting and classifying epithelial and stromal regions in histopathological images. Neurocomputing, 2016, 191(3): 214-223.
9. Yu K H, Zhang Ce, Berry G J, et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun, 2016, 7(5): 12474.
10. Vaidya P, Wang X, Bera K, et al. RaPtomics: integrating radiomic and pathomic features for predicting recurrence in early stage lung cancer. Digital Pathology, 2018, 6(2): 105-118.
11. Aerts H J, Grossmann P, Tan Yongqiang, et al. Defining a radiomic response phenotype: a pilot study using targeted therapy in NSCLC. Sci Rep, 2016, 6(2): 33860.
12. Liu Ying, Kim J, Balagurunathan Y, et al. Radiomic features are associated with EGFR mutation status in lung adenocarcinomas. Clin Lung Cancer, 2016, 17(5): 441-448.
13. Isola P, Zhu Junyan, Zhou Tinghui, et al. Image-to-image translation with conditional adversarial networks//30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), 2017: 5967-5976.
14. Khan A M, Rajpoot N, Treanor D, et al. A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution. IEEE Trans Biomed Eng, 2014, 61(6): 1729-1738.
15. Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation//Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer, 2015, 9351: 234-241.
16. Ali S, Lewis J, Madabhushi A. Spatially aware cell cluster graphs: predicting outcome in oropharyngeal pl6+ tumors//International Conference on Medical Image Computing and Computer-Assisted Intervention, Berlin: Springer, 2013: 412-419.
17. Haralick R M, Shanmugam K, Dinstein H. Textural features for image classification. IEEE Transactions on Systems, 1973, 3(6): 610-621.
18. Duyckaerts C, Godefroy G. Voronoi tessellation to study the numerical density and the spatial distribution of neurones. J Chem Neuroanat, 2000, 20(1): 83-92.
19. Lee G, Ali S, Veltri R, et al. Cell orientation entropy (COrE): predicting biochemical recurrence from prostate cancer tissue microarrays. Med Image Comput Comput Assist Interv, 2013, 16(3): 396-403.
20. Peng Hanchuan, Long Fuhui, Ding C. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell, 2005, 27(8): 1226-1238.
21. Suykens J, Vandewalle J. Least squares support vector machine classifiers. Neural Processing Letters, 1999, 9(3): 293-300.
22. Furey T S, Cristianini N, Duffy N, et al. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics, 2000, 16(10): 906-914.
23. Tong S, Koller D. Support vector machine active learning with applications to text classification. Journal of Machine Learning Research, 2002, 15(21): 999-1006.
24. Ginsburg S B, Viswanath S E, Bloch B, et al. Novel PCA-VIP scheme for ranking MRI protocols and identifying computer-extracted MRI measurements associated with central gland and peripheral zone prostate tumors. Journal of Magnetic Resonance Imaging, 2015, 41(5): 1383-1393.

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