413 related articles for article (PubMed ID: 32663813)
1. 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]
2. 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]
3. 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]
4. A comprehensive comparison of IMRT and VMAT plan quality for prostate cancer treatment.
Quan EM; Li X; Li Y; Wang X; Kudchadker RJ; Johnson JL; Kuban DA; Lee AK; Zhang X
Int J Radiat Oncol Biol Phys; 2012 Jul; 83(4):1169-78. PubMed ID: 22704703
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. Towards automated on-line adaptation of 2-Step IMRT plans: QUASIMODO phantom and prostate cancer cases.
Holubyev K; Bratengeier K; Gainey M; Polat B; Flentje M
Radiat Oncol; 2013 Nov; 8():263. PubMed ID: 24207129
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Using deep learning to predict beam-tunable Pareto optimal dose distribution for intensity-modulated radiation therapy.
Bohara G; Sadeghnejad Barkousaraie A; Jiang S; Nguyen D
Med Phys; 2020 Sep; 47(9):3898-3912. PubMed ID: 32621789
[TBL] [Abstract][Full Text] [Related]
9. Accelerate treatment planning process using deep learning generated fluence maps for cervical cancer radiation therapy.
Yuan Z; Wang Y; Hu P; Zhang D; Yan B; Lu HM; Zhang H; Yang Y
Med Phys; 2022 Apr; 49(4):2631-2641. PubMed ID: 35157337
[TBL] [Abstract][Full Text] [Related]
10. Study on the transferability of the knowledge-based VMAT model to predict IMRT plans in prostate cancer radiotherapy.
Bi S; Sun X; Sohaimi WFBW; Yusoff ALB
Eur J Med Res; 2023 Aug; 28(1):309. PubMed ID: 37653551
[TBL] [Abstract][Full Text] [Related]
11. Fluence-map generation for prostate intensity-modulated radiotherapy planning using a deep-neural-network.
Lee H; Kim H; Kwak J; Kim YS; Lee SW; Cho S; Cho B
Sci Rep; 2019 Oct; 9(1):15671. PubMed ID: 31666647
[TBL] [Abstract][Full Text] [Related]
12. Clinical validation and benchmarking of knowledge-based IMRT and VMAT treatment planning in pelvic anatomy.
Hussein M; South CP; Barry MA; Adams EJ; Jordan TJ; Stewart AJ; Nisbet A
Radiother Oncol; 2016 Sep; 120(3):473-479. PubMed ID: 27427380
[TBL] [Abstract][Full Text] [Related]
13. Intensity-modulated radiation therapy for pancreatic and prostate cancer using pulsed low-dose rate delivery techniques.
Li J; Lang J; Wang P; Kang S; Lin MH; Chen X; Chen F; Guo M; Chen L; Ma CM
Med Dosim; 2014; 39(4):330-6. PubMed ID: 25087084
[TBL] [Abstract][Full Text] [Related]
14. A quantitative study of IMRT delivery effects in commercial planning systems for the case of oesophagus and prostate tumours.
Seco J; Clark CH; Evans PM; Webb S
Br J Radiol; 2006 May; 79(941):401-8. PubMed ID: 16632620
[TBL] [Abstract][Full Text] [Related]
15. Automated generation of IMRT treatment plans for prostate cancer patients with metal hip prostheses: comparison of different planning strategies.
Voet PW; Dirkx ML; Breedveld S; Heijmen BJ
Med Phys; 2013 Jul; 40(7):071704. PubMed ID: 23822408
[TBL] [Abstract][Full Text] [Related]
16. Advancing knowledge-based intensity modulated proton planning for adaptive treatment of high-risk prostate cancer.
Johnson CL; Hasan S; Huang S; Lin H; Gorovets D; Shim A; Apgar T; Yu F; Tsai P
Med Dosim; 2024 Spring; 49(1):19-24. PubMed ID: 37914563
[TBL] [Abstract][Full Text] [Related]
17. Impact of database quality in knowledge-based treatment planning for prostate cancer.
Wall PDH; Carver RL; Fontenot JD
Pract Radiat Oncol; 2018; 8(6):437-444. PubMed ID: 29730280
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Treatment planning comparison of IMPT, VMAT and 4π radiotherapy for prostate cases.
Tran A; Zhang J; Woods K; Yu V; Nguyen D; Gustafson G; Rosen L; Sheng K
Radiat Oncol; 2017 Jan; 12(1):10. PubMed ID: 28077128
[TBL] [Abstract][Full Text] [Related]
20. A feasibility study of automated inverse treatment planning for cancer of the prostate.
Reinstein LE; Wang XH; Burman CM; Chen Z; Mohan R; Kutcher G; Leibel SA; Fuks Z
Int J Radiat Oncol Biol Phys; 1998 Jan; 40(1):207-14. PubMed ID: 9422578
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]