Abstract The design of a conventional radiotherapy plan is a time-consuming and labor-intensive process, and relevant parameters need to be continuously adjusted in the plan optimization to identify the optimal plan. In addition, experience differences between planners, time invested in plan design, and institutional standards all affect the quality of the plan, which in turn influences clinical outcomes and patient prognosis. In recent years, automatic planning has developed rapidly, which can improve the efficiency of planning design while ensuring the quality of the plan. At present, there are several methods dedicated to the automation of radiotherapy planning design, such as the Rapid Plan and Auto-Planning functions in Eclipse and Pinnacle commercial treatment planning systems, and there are also studies applying artificial intelligence technology in dose prediction to achieve automatic planning. In this article, the research progress on automatic radiotherapy planning was reviewed, and the realization principles, clinical efficacy and existing problems of various automatic planning methods were illustrated.
Fund:National Natural Science Foundation of China (81071237, 81372420)
Corresponding Authors:
Yang Ruijie,Email:ruijyang@yahoo.com
Cite this article:
Zhang Qilin,Zhang Shuming,Wang Mingqing et al. Research progress on automatic treatment planning methods for radiotherapy[J]. Chinese Journal of Radiation Oncology, 2021, 30(3): 316-320.
Zhang Qilin,Zhang Shuming,Wang Mingqing et al. Research progress on automatic treatment planning methods for radiotherapy[J]. Chinese Journal of Radiation Oncology, 2021, 30(3): 316-320.
[1] Petrovic S,Khussainova G,Jagannathan R.Knowledge-light adaptation approaches in case-based reasoning for radiotherapy treatment planning[J].Artif Intell Med,2016,68:17-28.DOI:10.1016/j.artmed.2016.01.006. [2] Mcintosh C,Purdie TG.Contextual atlas regression forests:multiple-atlas-based automated dose prediction in radiation therapy[J].IEEE Trans Med Imaging,2016,35(4):1000-1012.DOI:10.1109/TMI.2015.2505188. [3] Sheng Y,Li T,Zhang Y,et al.Atlas-guided prostate intensity modulated radiation therapy (IMRT) planning[J].Phys Med Biol,2015,60(18):7277-7291.DOI:10.1088/0031-9155/60/18/7277. [4] Moore KL,Brame RS,Low DA,et al.Experience-based quality control of clinical intensity-modulated radiotherapy planning[J].Int J Radiat Oncol Biol Phys,2011,81(2):545-551.DOI:10.1016/j.ijrobp.2010.11.030. [5] Tol JP,Delaney AR,Dahele M,et al.Evaluation of a knowledge-based planning solution for head and neck cancer[J].Int J Radiat Oncol Biol Phys,2015,91(3):612-620.DOI:10.1016/j.ijrobp.2014.11.014. [6] Delaney AR,Tol JP,Dahele M,et al.Effect of dosimetric outliers on the performance of a commercial knowledge-based planning solution[J].Int J Radiat Oncol Biol Phys,2016,94(3):469-477.DOI:10.1016/j.ijrobp.2015.11.011. [7] Fogliata A,Reggiori G,Stravato A,et al.RapidPlan head and neck model:the objectives and possible clinical benefit[J].Radiat Oncol,2017,12(1):73.DOI:10.1186/s13014-017-0808-x. [8] Wu H,Jiang F,Yue H,et al.Applying a RapidPlan model trained on a technique and orientation to another:a feasibility and dosimetric evaluation[J].Radiat Oncol,2016,11(1):108.DOI:10.1186/s13014-016-0684-9. [9] Zhang X,Li X,Quan EM,et al.A methodology for automatic intensity-modulated radiation treatment planning for lung cancer[J].Phys Med Biol,2011,56(13):3873-3893.DOI:10.1088/0031-9155/56/13/009. [10] Boylan C,Rowbottom C.A bias-free,automated planning tool for technique comparison in radiotherapy - application to nasopharyngeal carcinoma treatments[J].J Appl Clin Med Phys,2014,15(1):213-225.DOI:10.1120/jacmp.v15i1.4530. [11] Tol JP,Dahele M,Peltola J,et al.Automatic interactive optimization for volumetric modulated arc therapy planning[J].Radiat Oncol,2015,10:75.DOI:10.1186/s13014-015-0388-6. [12] Hansen CR,Bertelsen A,Hazell I,et al.Automatic treatment planning improves the clinical quality of head and neck cancer treatment plans[J].Clin Transl Radiat Oncol,2016,1:2-8.DOI:10.1016/j.ctro.2016.08.001. [13] Hazell I,Bzdusek K,Kumar P,et al.Automatic planning of head and neck treatment plans[J].J Appl Clin Med Phys,2016,17(1):272-282.DOI:10.1120/jacmp.v17i1.5901. [14] Speer S,Klein A,Kober L,et al.Automation of radiation treatment planning:evaluation of head and neck cancer patient plans created by the Pinnacle (3) scripting and auto-planning functions[J].Strahlenther Onkol,2017,193(8):656-665.DOI:10.1007/s00066-017-1150-9. [15] Gintz D,Latifi K,Caudell J,et al.Initial evaluation of automated treatment planning software[J].J Appl Clin Med Phys,2016,17(3):331-346.DOI:10.1120/jacmp.v17i3.6167. [16] Kusters JMAM,Bzdusek K,Kumar P,et al.Correction to:automated IMRT planning in Pinnacle-a study in head-and-neck cancer[J].Strahlentherapie und Onkologie,2017,193(12):1077-1078.DOI:10.1007/s00066-017-1230-x. [17] Krayenbuehl J,Di Martino M,Guckenberger M,et al.Improved plan quality with automated radiotherapy planning for whole brain with hippocampus sparing:a comparison to the RTOG 0933 trial[J].Radiat Oncol,2017,12(1):161.DOI:10.1186/s13014-017-0896-7. [18] Nawa K,Haga A,Nomoto A,et al.Evaluation of a commercial automatic treatment planning system for prostate cancers[J].Med Dosim,2017,42(3):203-209.DOI:10.1016/j.meddos.2017.03.004. [19] Wang S,Zheng D,Zhang C,et al.Automatic planning on hippocampal avoidance whole-brain radiotherapy[J].Med Dosim,2017,42(1):63-68.DOI:10.1016/j.meddos.2016.12.002. [20] Song Y,Wang Q,Jiang X,et al.Fully automatic volumetric modulated arc therapy plan generation for rectal cancer[J].Radiother Oncol,2016,119(3):531-536.DOI:10.1016/j.radonc.2016.04.010. [21] Li X,Wang L,Wang J,et al.Dosimetric benefits of automation in the treatment of lower thoracic esophageal cancer:is manual planning still an alternative option?[J].Med Dosim,2017,42(4):289-295.DOI:10.1016/j.meddos.2017.06.004. [22] Hansen CR,Nielsen M,Bertelsen AS,et al.Automatic treatment planning facilitates fast generation of high-quality treatment plans for esophageal cancer[J].Acta Oncologica,2017,56(11):1495-1500.DOI:10.1080/0284186X.2017.1349928. [23] Zhu X,Ge Y,Li T,et al.A planning quality evaluation tool for prostate adaptive IMRT based on machine learning[J].Med Phys,2011,38(2):719-726.DOI:10.1118/1.3539749. [24] Boutilier JJ,Lee T,Craig T,et al.Models for predicting objective function weights in prostate cancer IMRT[J].Med Phys,2015,42(4):1586-1595.DOI:10.1118/1.4914140. [25] Ma M,Kovalchuk N,Buyyounouski MK,et al.Dosimetric features-driven machine learning model for DVH prediction in VMAT treatment planning[J].Med Phys,2019,46(2):857-867.DOI:10.1002/mp.13334. [26] Bai X,Shan G,Chen M,et al.Approach and assessment of automated stereotactic radiotherapy planning for early stage non-small-cell lung cancer[J].Biomed Eng Online,2019,18(1):101.DOI:10.1186/s12938-019-0721-7. [27] Shiraishi S,Moore KL.Knowledge-based prediction of three-dimensional dose distributions for external beam radiotherapy[J].Med Phys,2016,43(1):378.DOI:10.1118/1.4938583. [28] Nguyen D,Jia X,Sher D,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):65020.DOI:10.1088/1361-6560/ab039b. [29] Nguyen D,Long T,Jia X,et al.A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning[J].Sci Rep,2019,9(1):1076.DOI:10.1038/s41598-018-37741-x. [30] Barragan-Montero AM,Nguyen D,Lu W,et al.Three-dimensional dose prediction for lung IMRT patients with deep neural networks:robust learning from heterogeneous beam configurations[J].Med Phys,2019,46(8):3679-3691.DOI:10.1002/mp.13597. [31] Fan J,Wang J,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. [32] Chen X,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. [33] Babier A,Mahmood R,Mcniven AL,et al.Knowledge-based automated planning with three-dimensional generative adversarial networks[J].Med Phys,2020,47(2):297-306.DOI:10.1002/mp.13896.
2021, Vol. 30 
