Application of deep learning in cancer prognosis prediction model_Journal of Biomedical Engineering

Authors：

CHEN Wen , WANG Xu , DUAN Huihong , ZHANG Xiaobing , DONG Ting ,  NIE Shengdong

Institute of Medical Imaging, University of Shanghai for Science and Technology, Shanghai 200093, P.R.China;

Corresponding author：

Keywords：

deep learning; prognosis; cancer prognosis model; precision medical treatment

DOI：

10.7507/1001-5515.201909066

Video：

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In recent years, deep learning has provided a new method for cancer prognosis analysis. The literatures related to the application of deep learning in the prognosis of cancer are summarized and their advantages and disadvantages are analyzed, which can be provided for in-depth research. Based on this, this paper systematically reviewed the latest research progress of deep learning in the construction of cancer prognosis model, and made an analysis on the strengths and weaknesses of relevant methods. Firstly, the construction idea and performance evaluation index of deep learning cancer prognosis model were clarified. Secondly, the basic network structure was introduced, and the data type, data amount, and specific network structures and their merits and demerits were discussed. Then, the mainstream method of establishing deep learning cancer prognosis model was verified and the experimental results were analyzed. Finally, the challenges and future research directions in this field were summarized and expected. Compared with the previous models, the deep learning cancer prognosis model can better improve the prognosis prediction ability of cancer patients. In the future, we should continue to explore the research of deep learning in cancer recurrence rate, cancer treatment program and drug efficacy evaluation, and fully explore the application value and potential of deep learning in cancer prognosis model, so as to establish an efficient and accurate cancer prognosis model and realize the goal of precision medicine.

Citation： CHEN Wen, WANG Xu, DUAN Huihong, ZHANG Xiaobing, DONG Ting, NIE Shengdong. Application of deep learning in cancer prognosis prediction model. Journal of Biomedical Engineering, 2020, 37(5): 918-929. doi: 10.7507/1001-5515.201909066 Copy

1.	Nagpal K, Foote D, Liu Yun, et al. Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer. NPJ Digit Med, 2019, 2(1): 1-10.
2.	McCulloch W S, Pitts W J B O M B. A logical calculus of the ideas immanent in nervous activity. Bull Math Biol, 1990, 52(1-2): 99-115; discussion 73-97.
3.	Hameed A A, Karlik B, Salman M S. Back-propagation algorithm with variable adaptive momentum. Knowledge-Based Systems, 2016, 114: 79-87.
4.	Hornik K, Stinchcombe M B, White H. Multilayer feedforward networks are universal approximators. Neur Netw, 1989, 2(5): 359-366.
5.	Jiang X, Pang Y, Li X, et al. Deep neural networks with Elastic Rectified Linear Units for object recognition. Neurocomputing, 2018, 275: 1132-1139.
6.	Greenspan H, Van Ginneken B, Summers R M. Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique. IEEE Trans Med Imaging, 2016, 35(5): 1153-1159.
7.	毛渤淳, 陈圣恺, 谢雨, 等. 经典深度学习算法对中文随机对照试验智能判别应用. 中国循证医学杂志, 2019, 19(11): 1262-1267.
8.	Bray F I, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 2018, 68(6): 394-424.
9.	Dagogo-Jack I, Shaw A T. Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol, 2018, 15(2): 81-94.
10.	常小丽, 朱丹, 任许利, 等. 全身麻醉复合硬膜外麻醉对肿瘤患者预后影响的 Meta 分析. 中国循证医学杂志, 2011, 11(8): 954-959.
11.	杨娟, 母齐鸣, 谭琴. 影响肝内胆管癌手术预后的危险因素 Logistic 回归分析. 江苏大学学报: 医学版, 2019, 29(2): 161-165.
12.	Jake L, Min W, Deepika G, et al. Big data application in biomedical research and health care: A literature review. Biomed Inform Insights, 2016, 8: 1-10.
13.	Chen Y C, Ke W C, Chiu H W, et al. Risk classification of cancer survival using ANN with gene expression data from multiple laboratories. Comput Biol Med, 2014, 48(1): 1-7.
14.	Giraud P, Giraud P, Gasnier A, et al. Radiomics and machine learning for radiotherapy in head and neck cancers. Front Oncol, 2019, 9: 174-184.
15.	Lambin P, Leijenaar R T H, Deist T M, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol, 2017, 14(12): 749-762.
16.	Arimura H, Soufi M, Kamezawa H, et al. Radiomics with artificial intelligence for precision medicine in radiation therapy. J Radiat Res, 2019, 60(1): 150-157.
17.	Kontos D, Summers M M D R, Giger M L. Special section guest editorial: Radiomics and deep learning. J Med Imaging, 2018, 4(4): 041301.
18.	Sun D, Wang M, Li A, et al. A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data. IEEE/ACM Trans Comput Biol Bioinform, 2019, 16(3): 841-850.
19.	Lai Y, Chen W, Hsu T, et al. Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning. Sci Rep, 2020, 10(1): 4679.
20.	Lee B, Chun S H, Hong J H, et al. DeepBTS: Prediction of recurrence-free survival of non-small cell lung cancer using a time-binned deep neural network. Sci Rep, 2020, 10(1): 1952.
21.	Zhu X, Yao J, Huang J. Deep convolutional neural network for survival analysis with pathological images// IEEE International Conference on Bioinformatics & Biomedicine. Shenzhen: IEEE, 2016: 544-547.
22.	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): 2970-2979.
23.	Paul R, Hawkins S H, Balagurunathan Y, et al. Deep feature transfer learning in combination with traditional features predicts survival among patients with lung adenocarcinoma. Tomography, 2016, 2(4): 388-395.
24.	Lao J, Chen Y, Li Z, et al. A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme. Sci Rep, 2017, 7(1): 10353.
25.	Han W, Qin L, Bay C, et al. Deep transfer learning and radiomics feature prediction of survival of patients with high-grade gliomas. Am J Neuroradiol, 2020, 41(1): 40-48.
26.	Bizzego A, Bussola N, Salvalai D, et al. Integrating deep and radiomics features in cancer bioimaging// 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). Siena: IEEE, 2019: 1-8.
27.	Tang Z, Xu Y, Jin L, et al. Deep learning of imaging phenotype and genotype for predicting overall survival time of glioblastoma patients. IEEE Trans Med Imaging, 2020, 39(6): 2100-2109.
28.	Hosny A, Parmar C, Coroller T P, et al. Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study. PLoS Med, 2018, 15(11): e1002711.
29.	Nie D, Lu J, Zhang H, et al. Multi-channel 3D deep feature learning for survival time prediction of brain tumor patients using multi-modal neuroimages. Sci Rep, 2019, 9(1): 1103.
30.	Diamant A, Chatterjee A, Vallières M, et al. Deep learning in head & neck cancer outcome prediction. Sci Rep, 2019, 9(1): 2764.
31.	Bychkov D, Linder N, Turkki R, et al. Deep learning based tissue analysis predicts outcome in colorectal cancer. Sci Rep, 2018, 8(1): 3395.
32.	Zhang D, Zou L, Zhou X, et al. Integrating feature selection and feature extraction methods with deep learning to predict clinical outcome of breast cancer. IEEE Access, 2018, 6: 28936-28944.
33.	Maggio V, Chierici M, Jurman G, et al. Distillation of the clinical algorithm improves prognosis by multi-task deep learning in high-risk Neuroblastoma. PloS One, 2018, 13(12): e0208924.
34.	Chaudhary K, Poirion O, Lu L, et al. Deep learning-based multi-omics integration robustly predicts survival in liver cancer. Clin Cancer Res, 2017, 24(6): 1248-1259.
35.	Wong K K L, Rostomily R C, Wong S T C. Prognostic gene discovery in glioblastoma patients using deep learning. Cancers, 2019, 11(1): 53-68.
36.	Xie G, Dong C, Kong Y, et al. Group lasso regularized deep learning for cancer prognosis from multi-omics and clinical features. Genes, 2019, 10(3): 240-255.
37.	Esteva A, Kuprel B, Novoa R A, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 2017, 542(7639): 115-118.
38.	Gu J, Wang Z, Kuen J, et al. Recent advances in convolutional neural networks. Pattern Recogn, 2018, 77: 354-377.
39.	Pan S J, Yang Q. A survey on transfer learning. IEEE Trans Knowl Data Eng, 2010, 22(10): 1345-1359.
40.	Lyu H, Lu H, Mou L. Learning a transferable change rule from a recurrent neural network for land cover change detection. Remote Sens, 2016, 8(6): 506-528.
41.	Greff K, Srivastava R K, Koutnik J, et al. LSTM: A search space odyssey. IEEE Trans Neur Netw, 2017, 28(10): 2222-2232.
42.	Liu W, Wang Z, Liu X, et al. A survey of deep neural network architectures and their applications. Neurocomputing, 2017, 234: 11-26.
43.	Ibrahim R, Yousri N A, Ismail M A, et al. Multi-level gene/MiRNA feature selection using deep belief nets and active learning// 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Chicago: IEEE, 2014: 3957-3960.
44.	Bluemke D A, Moy L, Bredella M A, et al. Assessing radiology research on artificial intelligence: A brief guide for authors, reviewers, and readers—From the Radiology editorial board. Radiology, 2020, 294(3): 487-489.
45.	Monkam P, Qi S, Ma H, et al. Detection and classification of pulmonary nodules using convolutional neural networks: A survey. IEEE Access, 2019, 7: 78075-78091.
46.	Azuaje F. Artificial intelligence for precision oncology: beyond patient stratification. NPJ Precis Oncol, 2019, 3(1): 6-11.
47.	Girardi D, Kung J, Kleiser R, et al. Interactive knowledge discovery with the doctor-in-the-loop: a practical example of cerebral aneurysms research. Brain Inform, 2016, 3(3): 133-143.
48.	Yasaka K, Akai H, Mackin D, et al. Precision of quantitative computed tomography texture analysis using image filtering: A phantom study for scanner variability. Medicine, 2018, 96(21): e6993.

1. Nagpal K, Foote D, Liu Yun, et al. Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer. NPJ Digit Med, 2019, 2(1): 1-10.
2. McCulloch W S, Pitts W J B O M B. A logical calculus of the ideas immanent in nervous activity. Bull Math Biol, 1990, 52(1-2): 99-115; discussion 73-97.
3. Hameed A A, Karlik B, Salman M S. Back-propagation algorithm with variable adaptive momentum. Knowledge-Based Systems, 2016, 114: 79-87.
4. Hornik K, Stinchcombe M B, White H. Multilayer feedforward networks are universal approximators. Neur Netw, 1989, 2(5): 359-366.
5. Jiang X, Pang Y, Li X, et al. Deep neural networks with Elastic Rectified Linear Units for object recognition. Neurocomputing, 2018, 275: 1132-1139.
6. Greenspan H, Van Ginneken B, Summers R M. Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique. IEEE Trans Med Imaging, 2016, 35(5): 1153-1159.
7. 毛渤淳, 陈圣恺, 谢雨, 等. 经典深度学习算法对中文随机对照试验智能判别应用. 中国循证医学杂志, 2019, 19(11): 1262-1267.
8. Bray F I, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 2018, 68(6): 394-424.
9. Dagogo-Jack I, Shaw A T. Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol, 2018, 15(2): 81-94.
10. 常小丽, 朱丹, 任许利, 等. 全身麻醉复合硬膜外麻醉对肿瘤患者预后影响的 Meta 分析. 中国循证医学杂志, 2011, 11(8): 954-959.
11. 杨娟, 母齐鸣, 谭琴. 影响肝内胆管癌手术预后的危险因素 Logistic 回归分析. 江苏大学学报: 医学版, 2019, 29(2): 161-165.
12. Jake L, Min W, Deepika G, et al. Big data application in biomedical research and health care: A literature review. Biomed Inform Insights, 2016, 8: 1-10.
13. Chen Y C, Ke W C, Chiu H W, et al. Risk classification of cancer survival using ANN with gene expression data from multiple laboratories. Comput Biol Med, 2014, 48(1): 1-7.
14. Giraud P, Giraud P, Gasnier A, et al. Radiomics and machine learning for radiotherapy in head and neck cancers. Front Oncol, 2019, 9: 174-184.
15. Lambin P, Leijenaar R T H, Deist T M, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol, 2017, 14(12): 749-762.
16. Arimura H, Soufi M, Kamezawa H, et al. Radiomics with artificial intelligence for precision medicine in radiation therapy. J Radiat Res, 2019, 60(1): 150-157.
17. Kontos D, Summers M M D R, Giger M L. Special section guest editorial: Radiomics and deep learning. J Med Imaging, 2018, 4(4): 041301.
18. Sun D, Wang M, Li A, et al. A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data. IEEE/ACM Trans Comput Biol Bioinform, 2019, 16(3): 841-850.
19. Lai Y, Chen W, Hsu T, et al. Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning. Sci Rep, 2020, 10(1): 4679.
20. Lee B, Chun S H, Hong J H, et al. DeepBTS: Prediction of recurrence-free survival of non-small cell lung cancer using a time-binned deep neural network. Sci Rep, 2020, 10(1): 1952.
21. Zhu X, Yao J, Huang J. Deep convolutional neural network for survival analysis with pathological images// IEEE International Conference on Bioinformatics & Biomedicine. Shenzhen: IEEE, 2016: 544-547.
22. 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): 2970-2979.
23. Paul R, Hawkins S H, Balagurunathan Y, et al. Deep feature transfer learning in combination with traditional features predicts survival among patients with lung adenocarcinoma. Tomography, 2016, 2(4): 388-395.
24. Lao J, Chen Y, Li Z, et al. A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme. Sci Rep, 2017, 7(1): 10353.
25. Han W, Qin L, Bay C, et al. Deep transfer learning and radiomics feature prediction of survival of patients with high-grade gliomas. Am J Neuroradiol, 2020, 41(1): 40-48.
26. Bizzego A, Bussola N, Salvalai D, et al. Integrating deep and radiomics features in cancer bioimaging// 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). Siena: IEEE, 2019: 1-8.
27. Tang Z, Xu Y, Jin L, et al. Deep learning of imaging phenotype and genotype for predicting overall survival time of glioblastoma patients. IEEE Trans Med Imaging, 2020, 39(6): 2100-2109.
28. Hosny A, Parmar C, Coroller T P, et al. Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study. PLoS Med, 2018, 15(11): e1002711.
29. Nie D, Lu J, Zhang H, et al. Multi-channel 3D deep feature learning for survival time prediction of brain tumor patients using multi-modal neuroimages. Sci Rep, 2019, 9(1): 1103.
30. Diamant A, Chatterjee A, Vallières M, et al. Deep learning in head & neck cancer outcome prediction. Sci Rep, 2019, 9(1): 2764.
31. Bychkov D, Linder N, Turkki R, et al. Deep learning based tissue analysis predicts outcome in colorectal cancer. Sci Rep, 2018, 8(1): 3395.
32. Zhang D, Zou L, Zhou X, et al. Integrating feature selection and feature extraction methods with deep learning to predict clinical outcome of breast cancer. IEEE Access, 2018, 6: 28936-28944.
33. Maggio V, Chierici M, Jurman G, et al. Distillation of the clinical algorithm improves prognosis by multi-task deep learning in high-risk Neuroblastoma. PloS One, 2018, 13(12): e0208924.
34. Chaudhary K, Poirion O, Lu L, et al. Deep learning-based multi-omics integration robustly predicts survival in liver cancer. Clin Cancer Res, 2017, 24(6): 1248-1259.
35. Wong K K L, Rostomily R C, Wong S T C. Prognostic gene discovery in glioblastoma patients using deep learning. Cancers, 2019, 11(1): 53-68.
36. Xie G, Dong C, Kong Y, et al. Group lasso regularized deep learning for cancer prognosis from multi-omics and clinical features. Genes, 2019, 10(3): 240-255.
37. Esteva A, Kuprel B, Novoa R A, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 2017, 542(7639): 115-118.
38. Gu J, Wang Z, Kuen J, et al. Recent advances in convolutional neural networks. Pattern Recogn, 2018, 77: 354-377.
39. Pan S J, Yang Q. A survey on transfer learning. IEEE Trans Knowl Data Eng, 2010, 22(10): 1345-1359.
40. Lyu H, Lu H, Mou L. Learning a transferable change rule from a recurrent neural network for land cover change detection. Remote Sens, 2016, 8(6): 506-528.
41. Greff K, Srivastava R K, Koutnik J, et al. LSTM: A search space odyssey. IEEE Trans Neur Netw, 2017, 28(10): 2222-2232.
42. Liu W, Wang Z, Liu X, et al. A survey of deep neural network architectures and their applications. Neurocomputing, 2017, 234: 11-26.
43. Ibrahim R, Yousri N A, Ismail M A, et al. Multi-level gene/MiRNA feature selection using deep belief nets and active learning// 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Chicago: IEEE, 2014: 3957-3960.
44. Bluemke D A, Moy L, Bredella M A, et al. Assessing radiology research on artificial intelligence: A brief guide for authors, reviewers, and readers—From the Radiology editorial board. Radiology, 2020, 294(3): 487-489.
45. Monkam P, Qi S, Ma H, et al. Detection and classification of pulmonary nodules using convolutional neural networks: A survey. IEEE Access, 2019, 7: 78075-78091.
46. Azuaje F. Artificial intelligence for precision oncology: beyond patient stratification. NPJ Precis Oncol, 2019, 3(1): 6-11.
47. Girardi D, Kung J, Kleiser R, et al. Interactive knowledge discovery with the doctor-in-the-loop: a practical example of cerebral aneurysms research. Brain Inform, 2016, 3(3): 133-143.
48. Yasaka K, Akai H, Mackin D, et al. Precision of quantitative computed tomography texture analysis using image filtering: A phantom study for scanner variability. Medicine, 2018, 96(21): e6993.

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