A review of machine learning in tumor radiotherapy_Journal of Biomedical Engineering

Authors：

ZHANG Junqian ¹ ,  ZHANG Yuan ¹ , YIN Yong ^2,3 ,  ZHU Jian ^2,3 , LI Baosheng ^2,3

1. School of Information Science and Engineering, University of Jinan, Jinan 250022, P.R.China;
2. Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Jinan 250117, P.R.China;
3. Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital Affiliated to Shandong University, Jinan 250117, P.R.China;

Corresponding author：

ZHANG Yuan, Email: yuan.zhang@ieee.org

Keywords：

cancer; radiotherapy; artificial intelligence; machine learning; deep learning; neural network

DOI：

10.7507/1001-5515.201810051

Video：

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Radiotherapy is one of the main treatments for tumor with increasingly high request for technique precision and the equipment stability. Machine learning may bring radiotherapy simplicity, individualization and precision, and may improve the automatic level of planning and quality assurance. Based on the process of radiotherapy, this paper reviews the applications and researches on machine learning, with an emphasis on deep learning, and proposes the prospects in the following aspects: segmentation of normal tissue and tumor, planning, treatment delivery, quality assurance and prognosis prediction.

Citation： ZHANG Junqian, ZHANG Yuan, YIN Yong, ZHU Jian, LI Baosheng. A review of machine learning in tumor radiotherapy. Journal of Biomedical Engineering, 2019, 36(5): 879-884. doi: 10.7507/1001-5515.201810051 Copy

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2.	Valdes G, Simone N C, Chen J, et al. Clinical decision support of radiotherapy treatment planning: a data-driven machine learning strategy for patient-specific dosimetric decision making. Radiotherapy and Oncology, 2017, 125(3): 392-397.
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4.	Dolz J, Betrouni N, Quidet M, et al. Stacking denoising auto-encoders in a deep network to segment the brainstem on MRI in brain cancer patients: a clinical study. Computerized Medical Imaging and Graphics, 2016, 52: 8-18.
5.	Litjens G, Kooi T, Bejnordi B E, et al. A survey on deep learning in medical image analysis. Med Image Anal, 2017, 42: 60-88.
6.	Lu F, Wu F, Hu P, et al. Automatic 3D liver location and segmentation via convolutional neural network and graph cut. Int J Comput Assist Radiol Surg, 2016, 12(2): 171-182.
7.	Hu Peijun, Wu Fa, Peng Jialin, et al. Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution. Phys Med Biol, 2016, 61(24): 8676-8698.
8.	Hu P, Wu F, Peng J, et al. Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets. International Journal of Computer Assisted Radiology & Surgery, 2017, 12(3): 399-411.
9.	Ibragimov B, Xing Lei. Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks. Med Phys, 2017, 44(2): 547-557.
10.	Guo Yanrong, Gao Yaozong, Shen Dinggang. Deformable MR prostate segmentation via deep feature learning and sparse patch matching. IEEE Trans Med Imaging, 2016, 35(4): 1077-1089.
11.	Pereira S, Pinto A, Alves V, et al. Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging, 2016, 35(5): 1240-1251.
12.	Kamnitsas K, Ledig C, Newcombe V F, et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal, 2017, 36: 61-78.
13.	Men Kuo, Dai Jianrong, Li Yexiong. Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks. Med Phys, 2017, 44(12): 6377-6389.
14.	Men Kuo, Zhang Tao, Chen Xinyuan, et al. Fully automatic and robust segmentation of the clinical target volume for radiotherapy of breast cancer using big data and deep learning. Physica Medica, 2018, 50: 13-19.
15.	van Mourik A M, Elkhuizen P H, Minkema D A, et al. Multiinstitutional study on target volume delineation variation in breast radiotherapy in the presence of guidelines. Radiotherapy and Oncology, 2010, 94(3): 286-291.
16.	Li Xiadong, Deng Ziheng, Deng Qinghua, et al. A novel deep learning framework for internal gross target volume definition from 4D computed tomography of lung cancer patients. IEEE Access, 2018, 6: 37775-37783.
17.	Dias J, Rocha H, Ferreira B, et al. A genetic algorithm with neural network fitness function evaluation for IMRT beam angle optimization. Central European Journal of Operations Research, 2014, 22(3): 431-455.
18.	Shiraishi S, Moore K L. Knowledge-based prediction of three-dimensional dose distributions for external beam radiotherapy. Med Phys, 2016, 43(1): 378-387.
19.	Han Xiao. MR-based synthetic CT generation using a deep convolutional neural network method. Med Phys, 2017, 44(4): 1408-1419.
20.	Maspero M, Savenije M H, Dinkla A M, et al. Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy. Phys Med Biol, 2018, 63(18): 185001.
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22.	Faught A M, Olsen L, Schubert L, et al. Functional-guided radiotherapy using knowledge-based planning. Radiotherapy and Oncology, 2018, 129(3): 494-498.
23.	Schwaab J, Prall M, Sarti C, et al. Ultrasound tracking for intra-fractional motion compensation in radiation therapy. Physica Medica, 2014, 30(5): 578-582.
24.	Bukovsky I, Homma N, Ichiji K, et al. A fast neural network approach to predict lung tumor motion during respiration for radiation therapy applications. Biomed Res Int, 2015, 2015: 489679.
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26.	Interian Y, Rideout V, Kearney V P, et al. Deep Nets vs expert designed features in medical physics: an IMRT QA case study. Med Phys, 2018, 45(6): 2672-2680.
27.	曾彪, 张九堂, 王晖, 等. 放疗中心安全防护与放疗质量控制的规范化管理探讨. 中国医疗设备, 2015, 30(7): 139-141.
28.	Wu Jian, Su Zhong, Li Zuofeng. A neural network-based 2D/3D image registration quality evaluator for pediatric patient setup in external beam radiotherapy. Journal of Applied Clinical Medical Physics, 2016, 17(1): 22-33.
29.	Li Qiongge, Chan M F. Predictive time-series modeling using artificial neural networks for Linac beam symmetry: an empirical study. Ann N Y Acad Sci, 2017, 1387(1): 84-94.
30.	Yahya N, Ebert M A, Bulsara M, et al. Statistical-learning strategies generate only modestly performing predictive models for urinary symptoms following external beam radiotherapy of the prostate: a comparison of conventional and machine-learning methods. Med Phys, 2016, 43(5): 2040-2052.
31.	Zhen Xin, Chen Jiawei, Zhong Zichun, et al. Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study. Phys Med Biol, 2017, 62(21): 8246-8263.
32.	Li Hongming, Galperin-Aizenberg M, Pryma D, et al. Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy. Radiotherapy and Oncology, 2018, 129(2): 218-226.

1. 乔延伟. 肿瘤精确放疗技术发展及应用现状. 中国医疗设备, 2014, 29(7): 73-76.
2. Valdes G, Simone N C, Chen J, et al. Clinical decision support of radiotherapy treatment planning: a data-driven machine learning strategy for patient-specific dosimetric decision making. Radiotherapy and Oncology, 2017, 125(3): 392-397.
3. Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks// NIPS'12 Proceedings of the 25th International Conference on Neural Information Processing Systems, Doha: Asia Pacific Neural Network Assembly, 2012: 1097-1105.
4. Dolz J, Betrouni N, Quidet M, et al. Stacking denoising auto-encoders in a deep network to segment the brainstem on MRI in brain cancer patients: a clinical study. Computerized Medical Imaging and Graphics, 2016, 52: 8-18.
5. Litjens G, Kooi T, Bejnordi B E, et al. A survey on deep learning in medical image analysis. Med Image Anal, 2017, 42: 60-88.
6. Lu F, Wu F, Hu P, et al. Automatic 3D liver location and segmentation via convolutional neural network and graph cut. Int J Comput Assist Radiol Surg, 2016, 12(2): 171-182.
7. Hu Peijun, Wu Fa, Peng Jialin, et al. Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution. Phys Med Biol, 2016, 61(24): 8676-8698.
8. Hu P, Wu F, Peng J, et al. Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets. International Journal of Computer Assisted Radiology & Surgery, 2017, 12(3): 399-411.
9. Ibragimov B, Xing Lei. Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks. Med Phys, 2017, 44(2): 547-557.
10. Guo Yanrong, Gao Yaozong, Shen Dinggang. Deformable MR prostate segmentation via deep feature learning and sparse patch matching. IEEE Trans Med Imaging, 2016, 35(4): 1077-1089.
11. Pereira S, Pinto A, Alves V, et al. Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging, 2016, 35(5): 1240-1251.
12. Kamnitsas K, Ledig C, Newcombe V F, et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal, 2017, 36: 61-78.
13. Men Kuo, Dai Jianrong, Li Yexiong. Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks. Med Phys, 2017, 44(12): 6377-6389.
14. Men Kuo, Zhang Tao, Chen Xinyuan, et al. Fully automatic and robust segmentation of the clinical target volume for radiotherapy of breast cancer using big data and deep learning. Physica Medica, 2018, 50: 13-19.
15. van Mourik A M, Elkhuizen P H, Minkema D A, et al. Multiinstitutional study on target volume delineation variation in breast radiotherapy in the presence of guidelines. Radiotherapy and Oncology, 2010, 94(3): 286-291.
16. Li Xiadong, Deng Ziheng, Deng Qinghua, et al. A novel deep learning framework for internal gross target volume definition from 4D computed tomography of lung cancer patients. IEEE Access, 2018, 6: 37775-37783.
17. Dias J, Rocha H, Ferreira B, et al. A genetic algorithm with neural network fitness function evaluation for IMRT beam angle optimization. Central European Journal of Operations Research, 2014, 22(3): 431-455.
18. Shiraishi S, Moore K L. Knowledge-based prediction of three-dimensional dose distributions for external beam radiotherapy. Med Phys, 2016, 43(1): 378-387.
19. Han Xiao. MR-based synthetic CT generation using a deep convolutional neural network method. Med Phys, 2017, 44(4): 1408-1419.
20. Maspero M, Savenije M H, Dinkla A M, et al. Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy. Phys Med Biol, 2018, 63(18): 185001.
21. Zhang Jiahan, Wu Q J, Xie Tianyi, et al. An ensemble approach to knowledge-based intensity-modulated radiation therapy planning. Frontiers in Oncology, 2018, 8: 57.
22. Faught A M, Olsen L, Schubert L, et al. Functional-guided radiotherapy using knowledge-based planning. Radiotherapy and Oncology, 2018, 129(3): 494-498.
23. Schwaab J, Prall M, Sarti C, et al. Ultrasound tracking for intra-fractional motion compensation in radiation therapy. Physica Medica, 2014, 30(5): 578-582.
24. Bukovsky I, Homma N, Ichiji K, et al. A fast neural network approach to predict lung tumor motion during respiration for radiation therapy applications. Biomed Res Int, 2015, 2015: 489679.
25. Carlson J N, Park J M, Park S Y, et al. A machine learning approach to the accurate prediction of multi-leaf collimator positional errors. Phys Med Biol, 2016, 61(6): 2514-2531.
26. Interian Y, Rideout V, Kearney V P, et al. Deep Nets vs expert designed features in medical physics: an IMRT QA case study. Med Phys, 2018, 45(6): 2672-2680.
27. 曾彪, 张九堂, 王晖, 等. 放疗中心安全防护与放疗质量控制的规范化管理探讨. 中国医疗设备, 2015, 30(7): 139-141.
28. Wu Jian, Su Zhong, Li Zuofeng. A neural network-based 2D/3D image registration quality evaluator for pediatric patient setup in external beam radiotherapy. Journal of Applied Clinical Medical Physics, 2016, 17(1): 22-33.
29. Li Qiongge, Chan M F. Predictive time-series modeling using artificial neural networks for Linac beam symmetry: an empirical study. Ann N Y Acad Sci, 2017, 1387(1): 84-94.
30. Yahya N, Ebert M A, Bulsara M, et al. Statistical-learning strategies generate only modestly performing predictive models for urinary symptoms following external beam radiotherapy of the prostate: a comparison of conventional and machine-learning methods. Med Phys, 2016, 43(5): 2040-2052.
31. Zhen Xin, Chen Jiawei, Zhong Zichun, et al. Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study. Phys Med Biol, 2017, 62(21): 8246-8263.
32. Li Hongming, Galperin-Aizenberg M, Pryma D, et al. Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy. Radiotherapy and Oncology, 2018, 129(2): 218-226.

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