[an error occurred while processing this directive] | [an error occurred while processing this directive]
Dose volume histogram prediction method for organ at risk in VMAT planning of nasopharyngeal carcinoma based on equivalent uniform dose
Li Huijuan1, Li Yang2, Zhuang Yongdong2, Chen Zhongben1
1School of Biomedical Engineering, Guangzhou Xinhua University, Guangzhou 510520, China; 2Department of Radiation Oncology, National Cancer Center / National Clinical Research Center for Cancer / Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
AbstractObjective To evaluate the practicability of dose volume histogram (DVH) prediction model for organ at risk (OAR) of radiotherapy plan by minimizing the cost function based on equivalent uniform dose (EUD). Methods A total of 66 nasopharyngeal carcinoma (NPC) patients received volume rotational intensity modulated arc therapy (VMAT) at Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences from 2020 to 2021 were retrospectively selected for this study. Among them, 50 patients were used to train the recurrent neutral network (RNN) model and the remaining 16 cases were used to test the model. DVH prediction model was constructed based on RNN. A three-dimensional equal-weighted 9-field conformal plan was designed for each patient. For each OAR, the DVHs of individual fields were acquired as the model input, and the DVH of VMAT plan was regarded as the expected output. The prediction error obtained by minimizing EUD-based cost function was employed to train the model. The prediction accuracy was characterized by the mean and standard deviation between predicted and true values. The plan was re-optimized for the test cases based on the DVH prediction results, and the consistency and variability of the EUD and DVH parameters of interest (e.g., maximum dose for serial organs such as the spinal cord) were compared between the re-optimized plan and the original plan of OAR by the Wilcoxon paired test and box line plots. Results The neural network obtained by training the cost function based on EUD was able to obtain better DVH prediction results. The new plan guided by the predicted DVH was in good agreement with the original plan: in most cases, the D98% in the planning target volume (PTV) was greater than 95% of the prescribed dose for both plans, and there was no significant difference in the maximum dose and EUD in the brainstem, spinal cord and lens (all P>0.05). Compared with the original plan, the average reduction of optic chiasm, optic nerves and eyes in the new plans reached more than 1.56 Gy for the maximum doses and more than 1.22 Gy for EUD, and the average increment of temporal lobes reached 0.60 Gy for the maximum dose and 0.30 Gy for EUD. Conclusion The EUD-based loss function improves the accuracy of DVH prediction, ensuring appropriate dose targets for treatment plan optimization and better consistency in the plan quality.
About author:: Li Huijuan and Li Yang are contributed equally to the article
Cite this article:
Li Huijuan,Li Yang,Zhuang Yongdong et al. Dose volume histogram prediction method for organ at risk in VMAT planning of nasopharyngeal carcinoma based on equivalent uniform dose[J]. Chinese Journal of Radiation Oncology, 2023, 32(5): 430-437.
Li Huijuan,Li Yang,Zhuang Yongdong et al. Dose volume histogram prediction method for organ at risk in VMAT planning of nasopharyngeal carcinoma based on equivalent uniform dose[J]. Chinese Journal of Radiation Oncology, 2023, 32(5): 430-437.
[1] Tham IW, Hee SW, Yeo RM, et al.Treatment of nasopharyngeal carcinoma using intensity-modulated radiotherapy-the national cancer centre singapore experience[J]. Int J Radiat Oncol Biol Phys, 2009,75(5):1481-1486. DOI: 10.1016/j.ijrobp.2009.01.018. [2] Batumalai V, Jameson MG, Forstner DF, et al.How important is dosimetrist experience for intensity modulated radiation therapy? A comparative analysis of a head and neck case[J]. Pract Radiat Oncol, 2013,3(3):e99-e106. DOI: 10.1016/j.prro.2012.06.009. [3] Berry SL, Boczkowski A, Ma R, et al.Interobserver variability in radiation therapy plan output: results of a single-institution study[J]. Pract Radiat Oncol, 2016,6(6):442-449. DOI: 10.1016/j.prro.2016.04.005. [4] Nelms BE, Robinson G, Markham J, et al.Variation in external beam treatment plan quality: an inter-institutional study of planners and planning systems[J]. Pract Radiat Oncol, 2012,2(4):296-305. DOI: 10.1016/j.prro.2011.11.012. [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] Tol JP, Dahele M, Delaney AR, et al.Can knowledge-based DVH predictions be used for automated, individualized quality assurance of radiotherapy treatment plans?[J]. Radiat Oncol, 2015,10:234. DOI: 10.1186/s13014-015-0542-1. [7] Chang A, Hung A, Cheung F, et al.Comparison of planning quality and efficiency between conventional and knowledge-based algorithms in nasopharyngeal cancer patients using intensity modulated radiation therapy[J]. Int J Radiat Oncol Biol Phys, 2016,95(3):981-990. DOI: 10.1016/j.ijrobp.2016.02.017. [8] 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. [9] Korani MM, Dong P, Xing L.Deep-learning based prediction of achievable dose for personalizing inverse treatment planning[J]. Med Phys, 2016, 43(6):3724-3724. DOI:10.1118/1.4957369. [10] Yu G, Li Y, Feng Z, et al.Knowledge-based IMRT planning for individual liver cancer patients using a novel specific model[J]. Radiat Oncol, 2018,13(1):52. DOI: 10.1186/s13014-018-0996-z. [11] 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. [12] Zhuang Y, Han J, Chen L, et al. Dose-volume histogram prediction in volumetric modulated arc therapy for nasopharyngeal carcinomas based on uniform-intensity radiation with equal angle intervals[J]. Phys Med Biol, 2019,64(23):23NT03. DOI: 10.1088/1361-6560/ab5433. [13] Cao W, Zhuang Y, Chen L, et al.Application of dose-volume histogram prediction in biologically related models for nasopharyngeal carcinomas treatment planning[J]. Radiat Oncol, 2020,15(1):216. DOI: 10.1186/s13014-020-01623-2. [14] Zhuang Y, Xie Y, Wang L, et al.DVH prediction for VMAT in NPC with GRU-RNN: an improved method by considering biological effects[J]. Biomed Res Int, 2021,2021:2043830. DOI: 10.1155/2021/2043830. [15] 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. [16] Wu B, Ricchetti F, Sanguineti G, et al.Patient geometry-driven information retrieval for IMRT treatment plan quality control[J]. Med Phys, 2009,36(12):5497-5505. DOI: 10.1118/1.3253464. [17] Wu B, Ricchetti F, Sanguineti G, et al.Data-driven approach to generating achievable dose-volume histogram objectives in intensity-modulated radiotherapy planning[J]. Int J Radiat Oncol Biol Phys, 2011,79(4):1241-1247. DOI: 10.1016/j.ijrobp.2010.05.026. [18] 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. [19] Yuan L, Ge Y, Lee WR, et al.Quantitative analysis of the factors which affect the interpatient organ-at-risk dose sparing variation in IMRT plans[J]. Med Phys, 2012,39(11):6868-6878. DOI: 10.1118/1.4757927. [20] Xing Y, Nguyen D, Lu W, et al.Technical note: a feasibility study on deep learning-based radiotherapy dose calculation[J]. Med Phys, 2020,47(2):753-758. DOI: 10.1002/mp.13953. [21] Ma M, K Buyyounouski M, Vasudevan V, et al. Dose distribution prediction in isodose feature-preserving voxelization domain using deep convolutional neural network[J]. Med Phys, 2019,46(7):2978-2987. DOI: 10.1002/mp.13618. [22] Barragán-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. [23] Niemierko A.A generalized concept of equivalent uniform dose (EUD)[J]. Med Phys, 1999,26(6):1100. [24] Park CS, Kim Y, Lee N, et al.Method to account for dose fractionation in analysis of IMRT plans: modified equivalent uniform dose[J]. Int J Radiat Oncol Biol Phys, 2005,62(3):925-932. DOI: 10.1016/j.ijrobp.2004.11.039. [25] Thieke C, Bortfeld T, Niemierko A, et al.From physical dose constraints to equivalent uniform dose constraints in inverse radiotherapy planning[J]. Med Phys, 2003,30(9):2332-2339. DOI: 10.1118/1.1598852. [26] Wu Q, Mohan R.Algorithms and functionality of an intensity modulated radiotherapy optimization system[J]. Med Phys, 2000,27(4):701-711. DOI: 10.1118/1.598932. [27] Wu Q, Mohan R, Niemierko A, et al.Optimization of intensity-modulated radiotherapy plans based on the equivalent uniform dose[J]. Int J Radiat Oncol Biol Phys, 2002,52(1):224-235. DOI: 10.1016/s0360-3016(01)02585-8. [28] Wu Q, Djajaputra D, Wu Y, et al.Intensity-modulated radiotherapy optimization with gEUD-guided dose-volume objectives[J]. Phys Med Biol, 2003,48(3):279-291. DOI: 10.1088/0031-9155/48/3/301. [29] Drossos K, Gharib S, Magron P, et al.Language modelling for sound event detection with teacher forcing and scheduled sampling[J/OL]. Computer Science.(2019-11-06).[2021-12-01]. https://doi.org/10.48550/arXiv.1907.08506. [30] Anon. 3. Special Considerations Regarding Absorbed- Dose and Dose-Volume Prescribing and Reporting in IMRT[J]. J ICRU, 2010,10(1):27-40. DOI: 10.1093/jicru/ndq008. [31] Allen Li X, Alber M, Deasy JO, et al.The use and QA of biologically related models for treatment planning: short report of the TG-166 of the therapy physics committee of the AAPM[J]. Med Phys, 2012,39(3):1386-1409. DOI: 10.1118/1.3685447.
2023, Vol. 32 
