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Planning evaluation of a novel volume-based algorithm for personalized optimization of lung dose in VMAT for esophageal cancer
Hsu, Chen Xiong1,2; Lin, Kuan Heng1,2,3; Wang, Shan Ying1,4; Tsai, Wei Ta1; Chang, Chiu Han2; Tien, Hui Ju1,2; Shueng, Pei Wei2,5; Wu, Tung Hsin1; Mok, Greta S.P.6
2022-02-15
Source PublicationScientific Reports
ISSN2045-2322
Volume12Issue:1Pages:2513
Abstract

Radiotherapy treatment planning (RTP) is time-consuming and labor-intensive since medical physicists must devise treatment plans carefully to reduce damage to tissues and organs for patients. Previously, we proposed the volume-based algorithm (VBA) method, providing optimal partial arcs (OPA) angle to achieve the low-dose volume of lungs in dynamic arc radiotherapy. This study aimed to implement the VBA for esophageal cancer (EC) patients and compare the lung dose and delivery time between full arcs (FA) without using VBA and OPA angle using VBA in volumetric modulated arc therapy (VMAT) plans. We retrospectively included 30 patients diagnosed with EC. RTP of each patient was replanned to 4 VMAT plans, including FA plans without (FA-C) and with (FA + C) dose constraints of OARs and OPA plans without (OPA-C) and with (OPA + C) dose constraints of OARs. The prescribed dose was 45 Gy. The OARs included the lungs, heart, and spinal cord. The dose distribution, dose-volume histogram, monitor units (MUs), delivery time, and gamma passing rates were analyzed. The results showed that the lung V and V in OPA + C plans were significantly lower than in FA + C plans (p < 0.05). No significant differences were noted in planning target volume (PTV) coverage, lung V, lung V, mean lung dose, heart V, heart V, mean heart dose, and maximal spinal cord dose between FA + C and OPA + C plans. The delivery time was significantly longer in FA + C plans than in OPA + C plans (237 vs. 192 s, p < 0.05). There were no significant differences between FA + C and OPA + C plans in gamma passing rates. We successfully applied the OPA angle based on the VBA to clinical EC patients and simplified the arc angle selection in RTP. The VBA could provide a personalized OPA angle for each patient and effectively reduce lung V, V and delivery time in VMAT.

DOI10.1038/s41598-021-04571-3
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000756701900010
Scopus ID2-s2.0-85124680388
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Citation statistics
Document TypeJournal article
CollectionFaculty of Science and Technology
DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING
Corresponding AuthorShueng, Pei Wei; Wu, Tung Hsin
Affiliation1.Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
2.Division of Radiation Oncology, Far Eastern Memorial Hospital, New Taipei City, Taiwan
3.Industrial Ph.D. Program of Biomedical Science and Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan
4.Department of Nuclear Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan
5.Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
6.Biomedical Imaging Laboratory, Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, SAR, Macao
Recommended Citation
GB/T 7714
Hsu, Chen Xiong,Lin, Kuan Heng,Wang, Shan Ying,et al. Planning evaluation of a novel volume-based algorithm for personalized optimization of lung dose in VMAT for esophageal cancer[J]. Scientific Reports, 2022, 12(1), 2513.
APA Hsu, Chen Xiong., Lin, Kuan Heng., Wang, Shan Ying., Tsai, Wei Ta., Chang, Chiu Han., Tien, Hui Ju., Shueng, Pei Wei., Wu, Tung Hsin., & Mok, Greta S.P. (2022). Planning evaluation of a novel volume-based algorithm for personalized optimization of lung dose in VMAT for esophageal cancer. Scientific Reports, 12(1), 2513.
MLA Hsu, Chen Xiong,et al."Planning evaluation of a novel volume-based algorithm for personalized optimization of lung dose in VMAT for esophageal cancer".Scientific Reports 12.1(2022):2513.
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