Study of automatic treatment planning of intensity-modulated radiotherapy based on deep learning technique for breast cancer patients
Fan Jiawei1, Chen Zhi2, Wang Jiazhou1, Hu Weigang1
1 Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College Fudan University, Shanghai 200032, China; 2 Department of Medical Physics, Shanghai Proton and Heavy Ion Center, Shanghai 201321, China
Abstract：Objective To develop a deep learning-based approach for predicting the dose distribution of intensity-modulated radiotherapy (IMRT) for breast cancer patients, and evaluate the feasibility of applying the predicted dose distribution in the automatic treatment planning. Methods A total of 240 patients with left breast cancer admitted to Fudan University Shanghai Cancer Center were enrolled in this study:200 cases in the training dataset, 20 cases in the validation dataset and 20 cases in the testing dataset. A modified deep residual neural network was trained to establish the relationship between CT image, the contouring images of target area and organs at risk (OARs) and the dose distribution, aiming to predict the dose distribution. The predicted dose distribution was utilized as the optimization objective function to optimize and generate a high-quality plan. Results Compared with the dose distribution of clinical treatment plan, the predicted dose distribution for target areas and OARs showed no statistical significance except for a simultaneous boost target PTV48Gy. And the treatment plan generated based on the predicted dose distribution was basically consistent with the predicted outcomes. Conclusion Our results demonstrate that the deep learning-based approach for predicting the dose distribution of IMRT for breast cancer contributes to further achieving the goal of automatic treatment planning.
Fan Jiawei,Chen Zhi,Wang Jiazhou et al. Study of automatic treatment planning of intensity-modulated radiotherapy based on deep learning technique for breast cancer patients[J]. Chinese Journal of Radiation Oncology, 2020, 29(8): 671-675.
[1] Li N, Noticevala SS, Willamson CW, et al. Feasibility of atlas-based active bone marrow sparing intensity modulated radiation therapy for cervical cancer[J]. Radiother Oncol, 2017, 123(2):325-330, DOI:10.1016/j.radonc.2017.02.017.
[2] Mclntosh C, Purdie TG. Voxel-based dose prediction with multi-patient atlas selection for automated radiotherapy treatment planning[J]. Phys Med Biol, 2017, 62(2):415-431. DOI:10.1088/1361-6560/62/2/415.
[3] Deshpande RR, DeMarco J, Sayre JW, et al. Knowledge-driven decision support for assessing dose distributions in radiation therapy of head and neck cancer[J]. Int J Comput Assist Radiol Surg, 2016, 11(11):2071-2083. DOI:10.1007/s11548-016-1403-6.
[4] Foy JJ, Marsh R, Ten Haken RK, et al. An analysis of knowledge-based planning for stereotactic body radiation therapy of the spine[J]. Pract Radiat Oncol, 2017, 7(5):355-360. DOI:10.1016/j.prro.2017.02.007.
[5] Dan N, Jia X, David S, et al. 3D radiotherapy dose prediction on head and neck cancer patients with a hierarchically densely connected U-net deep learning architecture[J]. Phys Med Biol, 2019, 64(6):227-234. DOI:10.1088/1361-6560/ab039b.
[6] Kearney V, Chan JW, Haaf S, et al. DoseNet:a volumetric dose prediction algorithm using 3D fully-convolutional neural networks[J]. Phys Med Biol, 2018, 63(23):601-610. DOI:10.1088/1361-6560/aaef74.
[7] Chen XY, Men K, Li Y, et al. A feasibility study on an automated method to generate patient specific dose distributions for radiotherapy using deep learning[J]. Med Phys, 2019, 46(1):56-64. DOI:10.1002/mp.13262.
[8] Liu ZQ, Fan JW, Li MH, et al. A deep learning method for prediction of three dimensional dose distribution of helical tomotherapy[J]. Med Phys, 2019, 46(5):1972-1983. DOI:10.1002/mp.13490.
[9] Fan JW, Wang JZ, Chen Z, et al. Automatic treatment planning based on three dimensional dose distribution predicted from deep learning technique[J]. Med Phys, 2019, 46(1):370-381. DOI:10.1002/mp.13271.
[10] Mao XJ, Shen CH, Yang YB, et al. Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections[J]. NeurIPS, 2016, 1(3):2-8. DOI:10.5555/3157382.3157412.
[11] Kingma D, Ba J. Adam:A method for stochastic optimization[C]. San Diego:Proceedings of the 3rd International Conference on Learning Representations, 2015. DOI:10.4236/am.2011.27123 3,919.
[12] Lyu JJ, Shao XH, Huang JS, et al. Data augmentation for face recognition[J]. Neurocomputing, 2017, 230(3):184-196. DOI:10.1016/j.neucom.2016.12.025.
[13] Wieser HP, Cisternas E, Wahl N, et al. Development of the open-source dose calculation and optimization toolkit matrad[J]. Med Phys, 2017, 44(1):2556-2568. DOI:10.1002/mp.12251.
2020, Vol. 29 
