Literature DB >> 31683261

Dose-volume histogram prediction in volumetric modulated arc therapy for nasopharyngeal carcinomas based on uniform-intensity radiation with equal angle intervals.

Yongdong Zhuang1, Junjie Han, Lixin Chen, Xiaowei Liu.   

Abstract

In this study, we developed a gated recurrent unit (GRU)-based recurrent neural network (RNN) for dose-volume histogram (DVH) prediction in volumetric modulated arc therapy (VMAT) planning for nasopharyngeal carcinomas (NPCs) based on uniform-intensity radiation with equal angle intervals and investigated the feasibility and usefulness of this method for treatment optimization. One hundred twenty-four NPC patients were selected from a database containing clinical VMAT plans from 2015 to 2018; of these, the data from 100 patients were used to train the GRU-RNN, and the data of the other 24 patients were used for testing. For the prescribed doses to D95 (the absorbed dose for 95% of the planning target volume) of all the plans in 30 or 31 fractions, 70 Gy were delivered to PTV70 (the gross tumour volume with circumferential margin), 60 Gy were delivered to PTV60, 54 Gy were delivered to PTV54 and 66 Gy were delivered to PTV66 (lymph node gross tumour volume with circumferential margin). For each NPC patient, an equal-field-weight conformal radiotherapy plan was generated by a treatment planning system (TPS) to offer uniform-intensity radiation. By adjusting the field weights, the dose distribution induced by individual conformal beams was acquired, and the corresponding DVH was calculated. Direction-dependent DVHs were employed to predict the DVH for VMAT with the GRU-RNN, and the regenerated VMAT experimental plans (EPs), guided by the predicted DVHs, were evaluated by comparing them with the clinical plans (CPs). For the 24 test patients, the regenerated EPs guided by the GRU-RNN predictive model achieved good consistency relative to the CPs. The EPs resulted in better dose sparing for many organs at risk (OARs) while still meeting the acceptable criteria for the PTVs. Significant differences were found in the maximum/mean doses to the optic nerves, temporal lobes, lenses, mandibles, temporomandibular joints (TMJs), larynx and inner ears, with P-values of 0.03, 0.01, 0.01, <0.01, 0.02, 0.02 and  <0.01, respectively. On average, compared to the CPs, the maximum/mean doses to these OARs were altered by  -1.38 Gy, -0.92 Gy, 0.53 Gy, -1.19 Gy, -1.16 Gy, 2.39 Gy and  -1.71 Gy, respectively. The results showed the accuracy and effectiveness of the proposed uniform-intensity radiation approach. The regenerated plans guided by the predictive method were not inferior to the manual plans, indicating their great potential for improved planning and quality control in clinical applications.

Entities:  

Mesh:

Year:  2019        PMID: 31683261     DOI: 10.1088/1361-6560/ab5433

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  3 in total

1.  DVH Prediction for VMAT in NPC with GRU-RNN: An Improved Method by Considering Biological Effects.

Authors:  Yongdong Zhuang; Yaoqin Xie; Luhua Wang; Shaomin Huang; Li-Xin Chen; Yuenan Wang
Journal:  Biomed Res Int       Date:  2021-01-19       Impact factor: 3.411

2.  A recurrent neural network for rapid detection of delivery errors during real-time portal dosimetry.

Authors:  James L Bedford; Ian M Hanson
Journal:  Phys Imaging Radiat Oncol       Date:  2022-04-20

3.  Application of dose-volume histogram prediction in biologically related models for nasopharyngeal carcinomas treatment planning.

Authors:  Wufei Cao; Yongdong Zhuang; Lixin Chen; Xiaowei Liu
Journal:  Radiat Oncol       Date:  2020-09-15       Impact factor: 3.481

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.