155 related articles for article (PubMed ID: 38509540)
1. Automatic IMRT treatment planning through fluence prediction and plan fine-tuning for nasopharyngeal carcinoma.
Cai W; Ding S; Li H; Zhou X; Dou W; Zhou L; Song T; Li Y
Radiat Oncol; 2024 Mar; 19(1):39. PubMed ID: 38509540
[TBL] [Abstract][Full Text] [Related]
2. Simultaneous dose distribution and fluence prediction for nasopharyngeal carcinoma IMRT.
Li Y; Cai W; Xiao F; Zhou X; Cai J; Zhou L; Dou W; Song T
Radiat Oncol; 2023 Jul; 18(1):110. PubMed ID: 37403141
[TBL] [Abstract][Full Text] [Related]
3. Dosimetric evaluation of automatic and manual plans for early nasopharyngeal carcinoma to radiotherapy.
Zhang Q; Peng Y; Song X; Yu H; Wang L; Zhang S
Med Dosim; 2020 Spring; 45(1):e13-e20. PubMed ID: 31466735
[TBL] [Abstract][Full Text] [Related]
4. Automatic dose prediction using deep learning and plan optimization with finite-element control for intensity modulated radiation therapy.
Shen Y; Tang X; Lin S; Jin X; Ding J; Shao M
Med Phys; 2024 Jan; 51(1):545-555. PubMed ID: 37748133
[TBL] [Abstract][Full Text] [Related]
5. A hybrid optimization strategy for deliverable intensity-modulated radiotherapy plan generation using deep learning-based dose prediction.
Sun Z; Xia X; Fan J; Zhao J; Zhang K; Wang J; Hu W
Med Phys; 2022 Mar; 49(3):1344-1356. PubMed ID: 35043971
[TBL] [Abstract][Full Text] [Related]
6. Volumetric intensity-modulated arc therapy vs conventional intensity-modulated radiation therapy in nasopharyngeal carcinoma: a dosimetric study.
White P; Chan KC; Cheng KW; Chan KY; Chau MC
J Radiat Res; 2013 May; 54(3):532-45. PubMed ID: 23188186
[TBL] [Abstract][Full Text] [Related]
7. Nasopharyngeal carcinoma radiotherapy with hybrid technique.
Akbas U; Koksal C; Kesen ND; Ozkaya K; Bilge H; Altun M
Med Dosim; 2019 Autumn; 44(3):251-257. PubMed ID: 30366620
[TBL] [Abstract][Full Text] [Related]
8. Automatic segmentation for adaptive planning in nasopharyngeal carcinoma IMRT: Time, geometrical, and dosimetric analysis.
Fung NTC; Hung WM; Sze CK; Lee MCH; Ng WT
Med Dosim; 2020 Spring; 45(1):60-65. PubMed ID: 31345672
[TBL] [Abstract][Full Text] [Related]
9. Dosimetric comparison between 2-dimensional radiation therapy and intensity modulated radiation therapy in treatment of advanced T-stage nasopharyngeal carcinoma: to treat less or more in the planning organ-at-risk volume of the brainstem and spinal cord.
Chau RM; Teo PM; Kam MK; Leung SF; Cheung KY; Chan AT
Med Dosim; 2007; 32(4):263-70. PubMed ID: 17980826
[TBL] [Abstract][Full Text] [Related]
10. Evaluation of automatic VMAT plans in locally advanced nasopharyngeal carcinoma.
Zhang Q; Ou L; Peng Y; Yu H; Wang L; Zhang S
Strahlenther Onkol; 2021 Mar; 197(3):177-187. PubMed ID: 32488293
[TBL] [Abstract][Full Text] [Related]
11. A knowledge-based intensity-modulated radiation therapy treatment planning technique for locally advanced nasopharyngeal carcinoma radiotherapy.
Bai P; Weng X; Quan K; Chen J; Dai Y; Xu Y; Lin F; Zhong J; Wu T; Chen C
Radiat Oncol; 2020 Aug; 15(1):188. PubMed ID: 32746873
[TBL] [Abstract][Full Text] [Related]
12. Determining appropriate timing of adaptive radiation therapy for nasopharyngeal carcinoma during intensity-modulated radiation therapy.
Huang H; Lu H; Feng G; Jiang H; Chen J; Cheng J; Pang Q; Lu Z; Gu J; Peng L; Deng S; Mo Y; Wu D; Wei Y
Radiat Oncol; 2015 Sep; 10():192. PubMed ID: 26377685
[TBL] [Abstract][Full Text] [Related]
13. Automatic IMRT planning via static field fluence prediction (AIP-SFFP): a deep learning algorithm for real-time prostate treatment planning.
Li X; Zhang J; Sheng Y; Chang Y; Yin FF; Ge Y; Wu QJ; Wang C
Phys Med Biol; 2020 Sep; 65(17):175014. PubMed ID: 32663813
[TBL] [Abstract][Full Text] [Related]
14. An artificial intelligence-driven agent for real-time head-and-neck IMRT plan generation using conditional generative adversarial network (cGAN).
Li X; Wang C; Sheng Y; Zhang J; Wang W; Yin FF; Wu Q; Wu QJ; Ge Y
Med Phys; 2021 Jun; 48(6):2714-2723. PubMed ID: 33577108
[TBL] [Abstract][Full Text] [Related]
15.
Qiu J; Lv B; Fu M; Wang X; Zheng X; Zhuo W
Head Neck; 2017 Dec; 39(12):2519-2527. PubMed ID: 28963789
[TBL] [Abstract][Full Text] [Related]
16. Dosimetry study of nasopharyngeal carcinoma based on Halcyon accelerator fixed-field intensity-modulated radiation therapy.
Li K; Li C; Zhu T; Chen J; Cao W; Yin Y; Ma C
J Cancer Res Ther; 2021 Nov; 17(5):1165-1171. PubMed ID: 34850763
[TBL] [Abstract][Full Text] [Related]
17. Prediction of dose-volume histograms in nasopharyngeal cancer IMRT using geometric and dosimetric information.
Jiao SX; Chen LX; Zhu JH; Wang ML; Liu XW
Phys Med Biol; 2019 Dec; 64(23):23NT04. PubMed ID: 31648210
[TBL] [Abstract][Full Text] [Related]
18. Attention-aware 3D U-Net convolutional neural network for knowledge-based planning 3D dose distribution prediction of head-and-neck cancer.
Osman AFI; Tamam NM
J Appl Clin Med Phys; 2022 Jul; 23(7):e13630. PubMed ID: 35533234
[TBL] [Abstract][Full Text] [Related]
19. Comparative evaluation of treatment plan quality for a prototype biology-guided radiotherapy system in the treatment of nasopharyngeal carcinoma.
Han C; Da Silva AJ; Liang J; Wohlers C; Huntzinger C; Neylon JP; Du D; Wong JYC; Liu A
Med Dosim; 2021 Summer; 46(2):171-178. PubMed ID: 33272744
[TBL] [Abstract][Full Text] [Related]
20. Dual arc volumetric-modulated arc radiotherapy (VMAT) of nasopharyngeal carcinomas: a simultaneous integrated boost treatment plan comparison with intensity-modulated radiotherapies and single arc VMAT.
Lee TF; Ting HM; Chao PJ; Fang FM
Clin Oncol (R Coll Radiol); 2012 Apr; 24(3):196-207. PubMed ID: 21752615
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
